[PERF] taking upper 32bit of PyObject_Hash into account
- [x] closes #37615
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry

Until now the upper 32bits of PyObject_Hash aren't taken into account at all. Because for my built-in objects the hash function is very simple (e.g. integers) many "normal" series would have all hashes being 0, which would lead to O(n^2) running times.

Thus for 64bit builds, we need to mangle the upper 32bit into the resulting 32bit-hash.

asv_bench gives:

```
       before           after         ratio
     [dae99c72]       [832d9d73]
+       123±0.4ms          367±6ms     2.99  series_methods.IsInLongSeriesValuesDominate.time_isin('object', 'monotone')
+      47.9±0.6ms          103±2ms     2.16  hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 1000000)
+     1.15±0.03ms      2.44±0.08ms     2.13  series_methods.IsInForObjects.time_isin_short_series_long_values
+     3.60±0.04ms       6.12±0.2ms     1.70  series_methods.IsInForObjects.time_isin_long_series_long_values
+     3.63±0.09ms      5.77±0.09ms     1.59  hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 100000)
+         283±4ms          385±3ms     1.36  series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'random_misses')
+      3.59±0.2μs       4.75±0.3μs     1.32  index_cached_properties.IndexCache.time_values('CategoricalIndex')
+      6.15±0.1ms       7.86±0.3ms     1.28  series_methods.IsInForObjects.time_isin_long_series_long_values_floats
+      44.3±0.7ms         56.1±1ms     1.27  categoricals.Indexing.time_reindex_missing
+      6.09±0.5μs       7.43±0.6μs     1.22  index_cached_properties.IndexCache.time_is_all_dates('DatetimeIndex')
+      3.54±0.2μs       4.26±0.3μs     1.20  index_cached_properties.IndexCache.time_inferred_type('MultiIndex')
+      5.41±0.6μs       6.50±0.5μs     1.20  index_cached_properties.IndexCache.time_shape('CategoricalIndex')
+      2.17±0.3μs       2.49±0.2μs     1.15  index_cached_properties.IndexCache.time_values('PeriodIndex')
+        461±10μs         527±10μs     1.14  hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 8000)
+     7.50±0.09ms      8.51±0.07ms     1.14  indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+        198±10μs         225±50μs     1.14  index_cached_properties.IndexCache.time_is_monotonic_increasing('DatetimeIndex')
+     8.35±0.07ms       9.45±0.1ms     1.13  multiindex_object.Integer.time_get_indexer
+      29.0±0.1ms       32.2±0.5ms     1.11  hash_functions.IsinWithArange.time_isin(<class 'object'>, 8000, 0)
+        916±40ns      1.02±0.05μs     1.11  index_cached_properties.IndexCache.time_is_monotonic_increasing('RangeIndex')
+      65.1±0.2ms         72.1±1ms     1.11  hash_functions.IsinWithArange.time_isin(<class 'object'>, 1000, 2)
+     1.23±0.05μs      1.36±0.09μs     1.11  index_cached_properties.IndexCache.time_is_monotonic_decreasing('RangeIndex')
+      3.18±0.3μs       3.52±0.3μs     1.11  index_cached_properties.IndexCache.time_shape('DatetimeIndex')
-     1.13±0.08ms         981±20μs     0.87  dtypes.SelectDtypes.time_select_dtype_string_include('float64')
-       106±0.5ms       91.6±0.7ms     0.87  hash_functions.IsinWithArange.time_isin(<class 'object'>, 2000, -2)
-        529±10μs         458±10μs     0.87  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 2000)
-        345±10μs          297±8μs     0.86  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 1300)
-        327±20μs          281±9μs     0.86  arithmetic.NumericInferOps.time_multiply(<class 'numpy.int8'>)
-        7.24±1μs       6.18±0.5μs     0.85  index_cached_properties.IndexCache.time_engine('PeriodIndex')
-         333±6μs          282±3μs     0.85  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 1300)
-     1.86±0.06ms      1.56±0.04ms     0.84  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 8000)
-         547±8μs          457±8μs     0.84  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 2000)
-      3.97±0.7μs       3.32±0.5μs     0.83  index_cached_properties.IndexCache.time_shape('PeriodIndex')
-     1.92±0.03ms      1.59±0.03ms     0.83  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 8000)
-     1.64±0.03ms      1.35±0.03ms     0.82  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 7000)
-      23.4±0.4ms         19.1±1ms     0.82  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 80000)
-     1.75±0.06ms      1.40±0.02ms     0.80  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 7000)
-      18.8±0.9ms       14.9±0.7ms     0.80  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 70000)
-        712±10ms          562±5ms     0.79  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 900000)
-         495±8ms         383±10ms     0.77  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 750000)
-         531±2ms          385±3ms     0.72  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 750000)
-        17.1±1ms       12.3±0.6ms     0.72  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 70000)
-         659±7ms          441±5ms     0.67  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 900000)
-      95.6±0.6ms       62.8±0.3ms     0.66  hash_functions.IsinWithArange.time_isin(<class 'object'>, 8000, -2)
-       138±0.7ms       87.1±0.7ms     0.63  hash_functions.IsinWithArange.time_isin(<class 'object'>, 2000, 2)
-         110±1ms       62.6±0.7ms     0.57  hash_functions.IsinWithArange.time_isin(<class 'object'>, 8000, 2)
-      72.2±0.3ms       12.0±0.3ms     0.17  hash_functions.IsinWithArange.time_isin(<class 'object'>, 1000, -2)
-         371±3ms         694±20μs     0.00  hash_functions.UniqueForLargePyObjectInts.time_unique
```

As always, when changing a hash functions, it is a mixed bag: for some cases the old hash-function was better, for some the new.

However, one can see, that the O(n^2) running time is avoided now:

```
-         371±3ms         694±20μs     0.00  hash_functions.UniqueForLargePyObjectInts.time_unique
``` > +       123±0.4ms          367±6ms     2.99  series_methods.IsInLongSeriesValuesDominate.time_isin('object', 'monotone')
+      47.9±0.6ms          103±2ms     2.16  hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 1000000)

are these degenerate?
 @jreback  

After thinking about it, I have opted for a slighly different hash-reduction from 64bit->32bit. It now has the advantages that it fixes the O(n^2) issue but also makes otherwise minimal changes to the behavior in other cases, thus keeping a better original performance in some corner cases you have highlighted.

The timings are now

```
       before           after         ratio
     [dae99c72]       [2fe320b7]
+      5.60±0.3μs       7.04±0.9μs     1.26  index_cached_properties.IndexCache.time_shape('TimedeltaIndex')
+      10.5±0.6μs         12.2±2μs     1.16  index_cached_properties.IndexCache.time_engine('TimedeltaIndex')
+      2.06±0.2μs       2.38±0.3μs     1.15  index_cached_properties.IndexCache.time_values('DatetimeIndex')
+     3.64±0.05ms      4.20±0.09ms     1.15  series_methods.IsInForObjects.time_isin_long_series_long_values
+      3.82±0.2μs       4.39±0.5μs     1.15  index_cached_properties.IndexCache.time_values('TimedeltaIndex')
+      3.51±0.2μs       3.96±0.4μs     1.13  index_cached_properties.IndexCache.time_inferred_type('Float64Index')
+      10.9±0.5μs       12.2±0.9μs     1.12  index_cached_properties.IndexCache.time_engine('UInt64Index')
+      4.85±0.4μs       5.37±0.3μs     1.11  index_cached_properties.IndexCache.time_shape('UInt64Index')
+        11.1±1μs       12.3±0.8μs     1.10  index_cached_properties.IndexCache.time_engine('Float64Index')
+         148±2ms          161±3ms     1.09  series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'random_hits')
+      6.28±0.5μs       6.82±0.6μs     1.09  index_cached_properties.IndexCache.time_is_all_dates('DatetimeIndex')
+      4.00±0.4μs       4.31±0.4μs     1.08  index_cached_properties.IndexCache.time_inferred_type('IntervalIndex')
+      12.1±0.7μs       12.9±0.7μs     1.07  index_cached_properties.IndexCache.time_is_all_dates('IntervalIndex')
+      9.87±0.6μs       10.5±0.4μs     1.06  index_cached_properties.IndexCache.time_is_all_dates('MultiIndex')
+         129±2ms          136±2ms     1.05  series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'monotone_hits')
-        700±40ns         664±30ns     0.95  index_cached_properties.IndexCache.time_is_unique('RangeIndex')
-     1.01±0.06μs         955±50ns     0.95  index_cached_properties.IndexCache.time_is_monotonic_increasing('RangeIndex')
-     1.82±0.04ms      1.72±0.01ms     0.94  hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 500000)
-     1.39±0.05μs      1.30±0.06μs     0.94  index_cached_properties.IndexCache.time_is_monotonic_decreasing('RangeIndex')
-      3.85±0.3μs       3.61±0.2μs     0.94  index_cached_properties.IndexCache.time_inferred_type('UInt64Index')
-         421±9ms          390±6ms     0.93  series_methods.IsInLongSeriesValuesDominate.time_isin('int32', 'random')
-     1.26±0.09μs      1.16±0.06μs     0.92  index_cached_properties.IndexCache.time_is_monotonic('RangeIndex')
-        739±50ns         678±30ns     0.92  index_cached_properties.IndexCache.time_is_unique('Int64Index')
-      3.95±0.3μs       3.61±0.2μs     0.91  index_cached_properties.IndexCache.time_values('Float64Index')
-      7.56±0.9μs       6.87±0.7μs     0.91  index_cached_properties.IndexCache.time_is_all_dates('PeriodIndex')
-      4.13±0.3μs       3.74±0.2μs     0.91  index_cached_properties.IndexCache.time_values('IntervalIndex')
-        773±30ns         692±30ns     0.89  index_cached_properties.IndexCache.time_inferred_type('Int64Index')
-        810±30ns         686±60ns     0.85  index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
-        353±20μs         294±10μs     0.84  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 1300)
-        547±40μs         450±10μs     0.82  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 2000)
-      19.2±0.6ms       15.1±0.7ms     0.78  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 70000)
-     1.79±0.09ms      1.40±0.05ms     0.78  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 7000)
-     1.98±0.04ms      1.54±0.05ms     0.78  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 8000)
-        17.3±1ms       13.2±0.8ms     0.76  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 70000)
-        741±20ms         561±10ms     0.76  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 900000)
-        503±20ms          379±9ms     0.75  hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 750000)
-         564±8ms         388±10ms     0.69  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 750000)
-         670±7ms          459±3ms     0.68  hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 900000)
-      73.2±0.8ms       11.9±0.2ms     0.16  hash_functions.IsinWithArange.time_isin(<class 'object'>, 1000, -2)
-      96.5±0.5ms       12.4±0.3ms     0.13  hash_functions.IsinWithArange.time_isin(<class 'object'>, 8000, -2)
-         107±1ms       12.0±0.5ms     0.11  hash_functions.IsinWithArange.time_isin(<class 'object'>, 2000, -2)
-         371±4ms         399±10μs     0.00  hash_functions.UniqueForLargePyObjectInts.time_unique
``` ok @realead looks good! can you add a whatsnew note (alt ok to just add this issue onto one of the previous ones for hashing). merge master and ping on greenish @jreback green thanks @realead 