ENH: RangeIndex._shallow_copy can return RangeIndex
- [x] closes #46675 (Replace xxxx with the Github issue number)
- [x] closes #43885 
- [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 an entry in the latest `doc/source/whatsnew/v1.5.0.rst` file if fixing a bug or adding a new feature.
> asv's prove out the change in perf?

The memory perf will just be evident in the result

```
In [1]: >>> idx1 = pd.RangeIndex(0, 100_000)
   ...: >>> idx2 = pd.RangeIndex(0, 50_000)

# main
In [2]: idx1.union(idx2, sort=False).memory_usage()
Out[2]: 800000

# PR
In [2]: idx1.union(idx2, sort=False).memory_usage()
Out[2]: 128
``` > idx1.union

no objection to this PR, but ideally this case would be caught in _union to prevent ever materializing the ndarray > > idx1.union
> 
> no objection to this PR, but ideally this case would be caught in _union to prevent ever materializing the ndarray

Added logic to do this in [63e7894](https://github.com/pandas-dev/pandas/pull/47557/commits/63e7894aadfc5db8462c8c4c67d960a9ed2c1731), but I think the change in `_shallow_copy` is still needed because concat goes though there > and asv's still hold here (after latest changes)?

```
% asv continuous -f 1.1 upstream/main HEAD -b index_object.Range
· Creating environments.........................
· Discovering benchmarks.
·· Uninstalling from conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.
·· Building 498b94f7 <enh/range_shallowcopy> for conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...........................................................................
·· Installing 498b94f7 <enh/range_shallowcopy> into conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
· Running 20 total benchmarks (2 commits * 1 environments * 10 benchmarks)
[  0.00%] · For pandas commit 8e6ca289 <main> (round 1/2):
[  0.00%] ·· Building for conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.........................................................................
[  0.00%] ·· Benchmarking conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[  2.50%] ··· Running (index_object.Range.time_get_loc_dec--)..........
[ 25.00%] · For pandas commit 498b94f7 <enh/range_shallowcopy> (round 1/2):
[ 25.00%] ·· Building for conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
[ 25.00%] ·· Benchmarking conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 27.50%] ··· Running (index_object.Range.time_get_loc_dec--)..........
[ 50.00%] · For pandas commit 498b94f7 <enh/range_shallowcopy> (round 2/2):
[ 50.00%] ·· Benchmarking conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 52.50%] ··· index_object.Range.time_get_loc_dec                                                                                                      554±100ns
[ 55.00%] ··· index_object.Range.time_get_loc_inc                                                                                                       509±50ns
[ 57.50%] ··· index_object.Range.time_iter_dec                                                                                                          14.4±2ms
[ 60.00%] ··· index_object.Range.time_iter_inc                                                                                                          15.9±1ms
[ 62.50%] ··· index_object.Range.time_max                                                                                                            1.20±0.09μs
[ 65.00%] ··· index_object.Range.time_max_trivial                                                                                                     1.10±0.2μs
[ 67.50%] ··· index_object.Range.time_min                                                                                                             1.24±0.1μs
[ 70.00%] ··· index_object.Range.time_min_trivial                                                                                                     1.48±0.3μs
[ 72.50%] ··· index_object.Range.time_sort_values_asc                                                                                                 2.73±0.2μs
[ 75.00%] ··· index_object.Range.time_sort_values_des                                                                                                   4.48±2μs
[ 75.00%] · For pandas commit 8e6ca289 <main> (round 2/2):
[ 75.00%] ·· Building for conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt...
[ 75.00%] ·· Benchmarking conda-py3.8-Cython0.29.30-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 77.50%] ··· index_object.Range.time_get_loc_dec                                                                                                       550±60ns
[ 80.00%] ··· index_object.Range.time_get_loc_inc                                                                                                      506±100ns
[ 82.50%] ··· index_object.Range.time_iter_dec                                                                                                          12.8±1ms
[ 85.00%] ··· index_object.Range.time_iter_inc                                                                                                          13.9±3ms
[ 87.50%] ··· index_object.Range.time_max                                                                                                             1.34±0.2μs
[ 90.00%] ··· index_object.Range.time_max_trivial                                                                                                    1.13±0.06μs
[ 92.50%] ··· index_object.Range.time_min                                                                                                             1.19±0.3μs
[ 95.00%] ··· index_object.Range.time_min_trivial                                                                                                    1.09±0.08μs
[ 97.50%] ··· index_object.Range.time_sort_values_asc                                                                                                 3.03±0.8μs
[100.00%] ··· index_object.Range.time_sort_values_des                                                                                                 5.22±0.5μs

BENCHMARKS NOT SIGNIFICANTLY CHANGED.
``` Thanks!