BUG: Preserve key order when using loc on MultiIndex DataFrame
This is another implementation to solve the issue described in #22797.

The other PR is #28933. When the other PR keep the main code identical and resort the indexer at the end, this implementation try to resolve the problem in the main loop of the original code.
The solution also as consequences on the performance when the key order is sorted as the index, so it add a flag to only keep the order if needed.

## Benchmark

<details>

Benchmarks that have improved:

       before           after         ratio
     [e623f0fb]       [b5b7be06]
     <stash~5>        <multiindex_loc_order_issue_22797>
-     2.50±0.01ms         2.35±0ms     0.94  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'int')
-     2.55±0.01ms         2.40±0ms     0.94  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'int')
-       767±0.5ms          707±2ms     0.92  groupby.Transform.time_transform_lambda_max
-      2.74±0.2μs       2.49±0.2μs     0.91  index_cached_properties.IndexCache.time_inferred_type('Float64Index')
-      2.64±0.1μs       2.51±0.2μs     0.95  index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
-        517±10μs         491±10μs     0.95  index_cached_properties.IndexCache.time_is_monotonic('MultiIndex')
-         671±6μs          605±6μs     0.90  index_cached_properties.IndexCache.time_is_monotonic_decreasing('MultiIndex')
-        514±10μs         489±10μs     0.95  index_cached_properties.IndexCache.time_is_monotonic_increasing('MultiIndex')
-      6.61±0.3μs         5.68±1μs     0.86  index_cached_properties.IndexCache.time_shape('TimedeltaIndex')
-      3.87±0.7μs       2.84±0.2μs     0.73  index_cached_properties.IndexCache.time_values('TimedeltaIndex')
-     2.87±0.03μs      2.72±0.01μs     0.95  indexing.GetItemSingleColumn.time_frame_getitem_single_column_label
-     1.19±0.02ms         787±20μs     0.66  indexing.MultiIndexing.time_frame_ix
-     1.11±0.02ms         722±20μs     0.65  indexing.MultiIndexing.time_series_ix
-     3.54±0.01ms      3.37±0.01ms     0.95  indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'unique_monotonic_inc')
-         644±2μs          568±1μs     0.88  multiindex_object.Duplicates.time_remove_unused_levels
-         746±8μs          189±2μs     0.25  multiindex_object.GetLoc.time_large_get_loc
-       169±0.6ms        102±0.4ms     0.61  multiindex_object.GetLoc.time_large_get_loc_warm
-      94.2±0.2μs       23.8±0.3μs     0.25  multiindex_object.GetLoc.time_med_get_loc
-      90.1±0.3ms       23.1±0.3ms     0.26  multiindex_object.GetLoc.time_med_get_loc_warm
-      87.1±0.2ms       22.8±0.2ms     0.26  multiindex_object.GetLoc.time_small_get_loc_warm
-      88.9±0.2μs       23.1±0.2μs     0.26  multiindex_object.GetLoc.time_string_get_loc
-       700±0.8μs        550±0.6μs     0.79  multiindex_object.Values.time_datetime_level_values_sliced
-     1.65±0.03ms      1.21±0.02ms     0.73  reindex.LevelAlign.time_align_level
-     1.65±0.04ms      1.22±0.01ms     0.74  reindex.LevelAlign.time_reindex_level
-      91.7±0.1ms       86.8±0.1ms     0.95  reshape.Crosstab.time_crosstab_normalize_margins
-      25.5±0.4ms       24.0±0.4ms     0.94  reshape.PivotTable.time_pivot_table
-      51.6±0.6ms       48.0±0.2ms     0.93  reshape.PivotTable.time_pivot_table_agg
-     19.7±0.05ms       18.4±0.2ms     0.93  reshape.PivotTable.time_pivot_table_categorical
-     14.2±0.03ms      13.1±0.04ms     0.92  reshape.PivotTable.time_pivot_table_categorical_observed
-       107±0.8ms       94.0±0.9ms     0.88  reshape.PivotTable.time_pivot_table_margins
-     6.02±0.02ms      5.47±0.02ms     0.91  reshape.SimpleReshape.time_stack
-     2.46±0.02ms      2.20±0.02ms     0.89  reshape.SimpleReshape.time_unstack
-        2.30±0ms      1.73±0.01ms     0.75  reshape.SparseIndex.time_unstack
-      18.9±0.1ms      18.0±0.03ms     0.95  rolling.Pairwise.time_pairwise(10, 'cov', True)
-        2.60±0ms         2.33±0ms     0.90  sparse.FromCoo.time_sparse_series_from_coo
-      41.3±0.2ms       39.2±0.2ms     0.95  sparse.ToCoo.time_sparse_series_to_coo
-         3.98±0s       3.75±0.01s     0.94  stat_ops.Correlation.time_corr_wide_nans('spearman', False)
-      3.99±0.01s       3.74±0.01s     0.94  stat_ops.Correlation.time_corr_wide_nans('spearman', True)
-     40.9±0.07ms       36.8±0.1ms     0.90  stat_ops.FrameMultiIndexOps.time_op(0, 'kurt')
-       195±0.3ms        165±0.3ms     0.85  stat_ops.FrameMultiIndexOps.time_op(1, 'kurt')
-        87.7±1ms         78.1±1ms     0.89  stat_ops.FrameMultiIndexOps.time_op(1, 'skew')
-         710±1ms          628±3ms     0.88  stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew')
-      10.7±0.1ms       9.66±0.3ms     0.90  stat_ops.SeriesMultiIndexOps.time_op(0, 'kurt')
-     10.6±0.05ms       9.15±0.2ms     0.86  stat_ops.SeriesMultiIndexOps.time_op(0, 'skew')
-      4.55±0.1ms      4.01±0.08ms     0.88  stat_ops.SeriesMultiIndexOps.time_op(0, 'std')
-     4.15±0.06ms       3.86±0.1ms     0.93  stat_ops.SeriesMultiIndexOps.time_op(0, 'var')
-      49.4±0.4ms       43.3±0.2ms     0.88  stat_ops.SeriesMultiIndexOps.time_op(1, 'kurt')
-       128±0.3ms        112±0.3ms     0.88  stat_ops.SeriesMultiIndexOps.time_op(1, 'mad')
-      48.0±0.3ms       40.3±0.4ms     0.84  stat_ops.SeriesMultiIndexOps.time_op(1, 'skew')
-     4.55±0.08ms       3.97±0.1ms     0.87  stat_ops.SeriesMultiIndexOps.time_op(1, 'std')
-      4.14±0.1ms      3.81±0.09ms     0.92  stat_ops.SeriesMultiIndexOps.time_op(1, 'var')
-       434±0.5ms        357±0.4ms     0.82  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'kurt')
-         1.20±0s          1.04±0s     0.86  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'mad')
-       422±0.7ms          339±1ms     0.80  stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'skew')
-      48.6±0.2ms      40.5±0.07ms     0.83  timeseries.DatetimeIndex.time_to_pydatetime('repeated')
-       288±0.9ms        271±0.8ms     0.94  timeseries.DatetimeIndex.time_to_pydatetime('tz_aware')
-      48.3±0.1ms       40.6±0.1ms     0.84  timeseries.DatetimeIndex.time_to_pydatetime('tz_naive')

Benchmarks that have got worse:

       before           after         ratio
     [e623f0fb]       [b5b7be06]
     <stash~5>        <multiindex_loc_order_issue_22797>
+       149±0.5ms        170±0.6ms     1.15  categoricals.Rank.time_rank_string
+      7.23±0.2ms       8.38±0.3ms     1.16  categoricals.Rank.time_rank_string_cat
+      9.81±0.1ms       10.4±0.1ms     1.06  categoricals.Rank.time_rank_string_cat_ordered
+     2.45±0.01ms      2.65±0.09ms     1.08  frame_methods.Iteration.time_items_cached
+       227±0.3ms          239±4ms     1.06  groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'transformation')
+       148±0.3ms        157±0.3ms     1.06  groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct')
+       148±0.5ms        157±0.8ms     1.06  groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'transformation')
+       144±0.5ms          153±3ms     1.06  groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct')
+       143±0.4ms          155±5ms     1.08  groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'transformation')
+         489±3μs          541±4μs     1.11  groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'direct')
+         488±2μs          546±3μs     1.12  groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'transformation')
+         481±2μs         547±20μs     1.14  groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'direct')
+         480±2μs          536±6μs     1.12  groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'transformation')
+       173±0.2ms        183±0.5ms     1.06  groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct')
+       173±0.5ms        183±0.4ms     1.06  groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'transformation')
+       334±0.3ms          358±3ms     1.07  groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct')
+       334±0.3ms          356±2ms     1.07  groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'transformation')
+       330±0.4ms          352±2ms     1.07  groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
+       331±0.7ms        353±0.6ms     1.07  groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'transformation')
+       212±0.3ms        224±0.8ms     1.06  groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct')
+       211±0.2ms        224±0.7ms     1.06  groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'transformation')
+         766±3μs         1.67±0ms     2.19  groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'direct')
+       766±0.7μs         1.68±0ms     2.19  groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'transformation')
+     4.96±0.05μs       5.42±0.1μs     1.09  index_cached_properties.IndexCache.time_engine('DatetimeIndex')
+     4.25±0.05μs      4.46±0.05μs     1.05  index_cached_properties.IndexCache.time_engine('PeriodIndex')
+      2.92±0.1μs       3.13±0.2μs     1.07  index_cached_properties.IndexCache.time_inferred_type('MultiIndex')
+        988±30ns      1.07±0.05μs     1.08  index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
+      2.38±0.1μs       2.74±0.5μs     1.15  index_cached_properties.IndexCache.time_inferred_type('TimedeltaIndex')
+      2.70±0.2μs       3.05±0.5μs     1.13  index_cached_properties.IndexCache.time_is_all_dates('UInt64Index')
+     1.41±0.02μs      1.51±0.04μs     1.07  index_cached_properties.IndexCache.time_is_monotonic_increasing('Int64Index')
+        941±20ns      1.01±0.02μs     1.07  index_cached_properties.IndexCache.time_is_unique('RangeIndex')
+      3.70±0.1μs       3.93±0.1μs     1.06  index_cached_properties.IndexCache.time_shape('UInt64Index')
+      2.88±0.2μs       3.05±0.2μs     1.06  index_cached_properties.IndexCache.time_values('UInt64Index')
+     5.43±0.08μs      6.39±0.02μs     1.18  index_object.Indexing.time_get_loc('Int')
+     5.44±0.06μs       6.45±0.2μs     1.19  index_object.Indexing.time_get_loc_sorted('Int')
+     2.76±0.06ms         3.01±0ms     1.09  indexing.IntervalIndexing.time_getitem_scalar
+     3.09±0.06ms      3.34±0.02ms     1.08  indexing.IntervalIndexing.time_loc_list
+     2.78±0.05ms         3.03±0ms     1.09  indexing.IntervalIndexing.time_loc_scalar
+     5.83±0.02ms      16.6±0.03ms     2.85  indexing.MultiIndexing.time_index_slice
+     2.45±0.02ms      2.59±0.02ms     1.06  inference.NumericInferOps.time_divide(<class 'numpy.uint8'>)
+     9.78±0.08ms      10.4±0.07ms     1.06  io.csv.ParseDateComparison.time_read_csv_dayfirst(False)
+         789±2ms          849±4ms     1.08  io.json.ReadJSON.time_read_json('index', 'datetime')
+       840±0.6ms          923±3ms     1.10  io.json.ReadJSON.time_read_json('index', 'int')
+     49.2±0.05ms       52.1±0.3ms     1.06  io.parsers.DoesStringLookLikeDatetime.time_check_datetimes('0.0')
+      68.1±0.4ms       72.3±0.5ms     1.06  io.parsers.DoesStringLookLikeDatetime.time_check_datetimes('2Q2005')
+        1.64±0μs       1.86±0.1μs     1.14  period.PeriodProperties.time_property('M', 'daysinmonth')
+     1.68±0.01μs      1.80±0.03μs     1.07  period.PeriodProperties.time_property('M', 'is_leap_year')
+     1.68±0.02μs      1.77±0.02μs     1.05  period.PeriodProperties.time_property('min', 'is_leap_year')
+       739±0.7ms          1.41±0s     1.91  stat_ops.FrameMultiIndexOps.time_op([0, 1], 'kurt')
+        764±10ns          810±2ns     1.06  timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
+        763±10ns        812±0.9ns     1.07  timestamp.TimestampProperties.time_freqstr(<UTC>, None)
+        763±10ns          812±1ns     1.06  timestamp.TimestampProperties.time_freqstr(None, None)
+        762±10ns          811±2ns     1.06  timestamp.TimestampProperties.time_freqstr(tzutc(), None)

</details>

## Checklist

- [x] closes #22797
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
can you merge master and ping on green for another look @jreback Of course. Hello @nrebena! Thanks for updating this PR. We checked the lines you've touched for [PEP 8](https://www.python.org/dev/peps/pep-0008) issues, and found:





There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: 

##### Comment last updated at 2020-01-11 10:43:08 UTC can you merge master and will look again I just realized you actually had 2 impls, sorry (#28933)

I think this one is more concise and easier to folow as it sorts in-line. which one do you prefer?

are the tests the same? (this doesn't look as updated). Test are the same, but not in sync yet.

I will update this one in priority now.

For pro and cons on each impl, this one is more concise, sort in place, but may lack flexibility to handle more complex sort scheme (if ever needed). The other add sorting as a complete separate step, so maybe have a better separation of concern at the cost of added complexity. > I just realized you actually had 2 impls, sorry (#28933)
> 
> I think this one is more concise and easier to folow as it sorts in-line. which one do you prefer?
> 
> are the tests the same? (this doesn't look as updated).

reading the other impl, i agree its simpler to grok. so changing my thoughts here. let's close this one and match tests on the other.