PERF: concat(axis=1) for objects with different indexes
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/v2.1.0.rst` file if fixing a bug or adding a new feature.

Perf improvement for `concat(objects, axis=1)` when `objects` have different indexes.  

```
> asv continuous -f 1.1 upstream/main perf-concat-axis-1 -b join_merge.ConcatIndexDtype

       before           after         ratio
     [e58d1ba1]       [b8fbc611]
     <main>           <perf-concat-axis-1>
-         121±9μs          109±3μs     0.90  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'has_na', 0, True)
-      2.67±0.2ms      2.40±0.02ms     0.90  join_merge.ConcatIndexDtype.time_concat_series('datetime64[ns]', 'non_monotonic', 1, False)
-     4.84±0.07ms      2.78±0.02ms     0.57  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'non_monotonic', 1, True)
-     4.14±0.03ms      2.25±0.04ms     0.54  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'monotonic', 1, True)
-     4.04±0.03ms      2.18±0.03ms     0.54  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'monotonic', 1, False)
-     5.70±0.05ms       2.82±0.2ms     0.50  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'has_na', 1, True)
-     4.27±0.08ms      2.11±0.03ms     0.49  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'non_monotonic', 1, False)
-     4.76±0.07ms      2.27±0.06ms     0.48  join_merge.ConcatIndexDtype.time_concat_series('int64', 'non_monotonic', 1, True)
-      4.34±0.1ms      2.01±0.03ms     0.46  join_merge.ConcatIndexDtype.time_concat_series('int64', 'monotonic', 1, False)
-     5.21±0.09ms       2.32±0.2ms     0.45  join_merge.ConcatIndexDtype.time_concat_series('Int64', 'has_na', 1, False)
-     4.33±0.03ms      1.93±0.03ms     0.45  join_merge.ConcatIndexDtype.time_concat_series('int64', 'non_monotonic', 1, False)
-      4.59±0.2ms      1.98±0.04ms     0.43  join_merge.ConcatIndexDtype.time_concat_series('int64', 'monotonic', 1, True)
-      20.2±0.2ms       3.94±0.3ms     0.19  join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'has_na', 1, True)
-      18.9±0.5ms      2.96±0.04ms     0.16  join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'non_monotonic', 1, True)
-      19.0±0.1ms      2.94±0.06ms     0.15  join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'has_na', 1, False)
-      18.2±0.1ms       2.67±0.2ms     0.15  join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'monotonic', 1, False)
-      18.2±0.1ms      2.38±0.03ms     0.13  join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'monotonic', 1, True)
-      18.1±0.1ms      2.28±0.04ms     0.13  join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'non_monotonic', 1, False)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
Thanks @lukemanley 