Bug: Save original index and remap after function completes
- [x] closes #55767
- [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/vX.X.X.rst` file if fixing a bug or adding a new feature.

Note: I'm new to this project, so this is my first PR.

Saves the index for SeriesNLargest at algorithm start and resets it before returning. This fixes performance issues when the index has many duplicate values.

### Results:
- The original statistics can be viewed in the original ticket, but slow_df was several ms. Note that the results in the ticket were not from my development machine and specific timings differ.
```
In [4]: import pandas as pd
   ...: import numpy as np
   ...: 
   ...: N = 1500
   ...: N_HALF = 750
   ...: 
   ...: slow_df = pd.DataFrame({'a':  np.random.rand(N)}, index=np.concatenate([[1] * N_HALF, np.arange(N_HALF)]))
   ...: print("slow_df")
   ...: %timeit slow_df['a'].nlargest()
   ...: 
   ...: fast_df = pd.DataFrame({'a': np.random.rand(N)})
   ...: print("fast_df")
   ...: %timeit fast_df['a'].nlargest()

slow_df
427 μs ± 11.4 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
fast_df
420 μs ± 5.4 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)

```

### Tests
The existing tests should cover this unless we want to add specific tests via the `asv_bench`.

### Addendum
I also modified the call to sort to use `sort(kind="stable")` to get consistent ordering which is what is currently happening in the equivalent Frame method (it was using `kind=mergesort` which is equivalent to `kind=stable`, but kept for portability). I can remove this -- it may be better in another PR.
https://numpy.org/doc/stable/reference/generated/numpy.sort.html#numpy.sort
> Thanks for the PR! I'm seeing a performance regression when the index does not contain duplicates. Can we do this conditionally.

I appreciate the review!

Can you confirm the performance regression numbers that you are seeing? Note that the timings in the original ticket were from a different machine than the one I'm working on.

ASV Benchmarks appear similar enough to be reasonable in the standard case.

-- Original Function --
```
[37.66%] ··· series_methods.NSort.time_nlargest                              ok
[37.66%] ··· ======= ==========
               keep            
             ------- ----------
              first   3.59±0ms 
               last   3.70±0ms 
               all    3.84±0ms 
             ======= ==========

[37.71%] ··· series_methods.NSort.time_nsmallest                             ok
[37.71%] ··· ======= ==========
               keep            
             ------- ----------
              first   3.26±0ms 
               last   2.94±0ms 
               all    3.31±0ms 
             ======= ==========
```
-- Function with my changes --
```
[37.66%] ··· series_methods.NSort.time_nlargest                              ok
[37.66%] ··· ======= ==========
               keep            
             ------- ----------
              first   3.71±0ms 
               last   3.41±0ms 
               all    3.60±0ms 
             ======= ==========

[37.71%] ··· series_methods.NSort.time_nsmallest                             ok
[37.71%] ··· ======= ==========
               keep            
             ------- ----------
              first   2.86±0ms 
               last   2.58±0ms 
               all    3.20±0ms 
             ======= ==========
``` > Thanks for the PR! I'm seeing a performance regression when the index does not contain duplicates. Can we do this conditionally.

Hey, can I get a re-review on this? Thanks for the ping, will get to it in the next day or two. Thanks @Jeffrharr 