BUG: GroupBy().fillna() performance regression
- [x] closes #36757 
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

A performance regression was introduced with #30679 in handling grouping on a duplicate axis. The regression can be avoided by skipping the call to `get_indexer_non_unique` if the axis is unchanged (i.e. like it is in `fillna`). 

I don't know how to write a test for this fix since it is just an issue of speed. If there is a test pattern or other check that should be included please highlight and I'm happy to add it. 

In lieu of a test, running the minimal example from the issue report on the fixed branch shows the performance fix:

```
In [5]: import pandas as pd
   ...: import numpy as np
   ...: 
   ...: N = 2000
   ...: df = pd.DataFrame({"A": [1] * N, "B": [np.nan, 1.0] * (N // 2)})
   ...: df = df.sort_values("A").set_index("A")
   ...: 
   ...: %time df.groupby("A")["B"].fillna(method="ffill")
   ...:
Wall time: 1.03 ms
Out[5]:
A
1    NaN
1    1.0
1    1.0
1    1.0
1    1.0
    ...
1    1.0
1    1.0
1    1.0
1    1.0
1    1.0
Name: B, Length: 2000, dtype: float64            
```