BUG: groupby with dropna=False drops nulls from categorical groupers
- [x] closes #36327 (Replace xxxx with the GitHub issue number)
- [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.

The function nunique here is faster than using `pd.unique`, but only works on integers:

```
arr = np.random.randint(0, 300, size)
%timeit len(unique(arr))
%timeit nunique_ints(arr)

size: 10
8.94 µs ± 133 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
2.94 µs ± 12.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

size: 100
9.82 µs ± 22.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
3.01 µs ± 5.16 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

size: 1000
11 µs ± 76.5 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
3.74 µs ± 9.7 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

size: 10000
27.9 µs ± 66.8 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
10.4 µs ± 9.43 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

size: 100000
187 µs ± 214 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
78.8 µs ± 408 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
```


Thanks @rhshadrach 