REF: avoid unnecessary argument to groupby numba funcs
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them
- [ ] whatsnew entry

cc @mroeschke im pretty sure this is slightly more correct than the status quo, but i could use some help in coming up with test cases.  Two main cases to cover:

1) `res = df.groupby(more_than_one_column).transform(..., engine="numba", i_dont_know_off_the_top_the_calling_convention)`

I'm pretty sure in this case `res.index.names` will be wrong in master.

2) Cases with BinGrouper where there is a length mismatch (also an issue with non-numba paths)

```
import pandas as pd

df = pd.DataFrame(
        {
            "Buyer": "Carl Carl Carl Carl Joe Carl".split(),
            "Quantity": [18, 3, 5, 1, 9, 3],
            "Date": [
                pd.Timestamp(2013, 9, 1, 13, 0),
                pd.Timestamp(2013, 9, 1, 13, 5),
                pd.Timestamp(2013, 10, 1, 20, 0),
                pd.Timestamp(2013, 10, 3, 10, 0),
                pd.NaT,
                pd.Timestamp(2013, 9, 2, 14, 0),
            ],
        }
    )


gb = df.groupby(pd.Grouper(key="Date", freq="5D"))

gb.transform(lambda x: x, engine="numba", ...)  # <- pretty sure this raises
```

Or are these not reachable for some reason?
For 1, these two examples are equivalent, but you mentioned the issue is in the `index.names`?

```
In [7]: def f(values, index):
   ...:     return values + 1
   ...:

In [8]: df = pd.DataFrame(np.eye(3))

In [9]: df.groupby([0, 1]).transform(f, engine="numba")
Out[9]:
     2
0  1.0
1  1.0
2  2.0

In [10]: df.groupby([0, 1]).transform(lambda x: x + 1)
Out[10]:
     2
0  1.0
1  1.0
2  2.0
```

For 2, that example should not work because the groups contain string data (also while debuging the timestamp data is also brought into the group). The numba engine can only work on numeric data. > For 1, these two examples are equivalent, but you mentioned the issue is in the index.names?

Looks like is just for aggregate, not transform.  My bad.

> For 2, that example should not work because the groups contain string data (also while debuging the timestamp data is also brought into the group). The numba engine can only work on numeric data.

OK.  If we just use the Quantity column:

```
>>> gb["Quantity"].aggregate(lambda values, index: values[0], engine="numba")
Date
NaT           18.0
2013-09-01     9.0
2013-09-06     9.0
2013-09-11     9.0
2013-09-16     9.0
2013-09-21     9.0
2013-09-26     5.0
2013-10-01     9.0
Name: Quantity, dtype: float64
```

NaT is in the resulting index, which I think is wrong, at least doesn't match the behavior of essentially all the non-numba aggregations.  (then again, that aggregation raises in this PR, which isn't a great sign).  

Can you suggest an aggregation that we can do on `gb["Quantity"]` that we can do with and without `engine="numba"` and expect the answers to match? So the thought is that `NaT` should be dropped since it was a "null" group? If so, probably a bug in the numba engine.

I think for this particular `df` and `gb["Quantity"]`, the numba and non-numba engine probably cannot be equal because of the bug above.

e.g. these are almost similar

```
In [10]: gb["Quantity"].aggregate(lambda values, index: np.nanmean(values), engine="numba")
Out[10]:
Date
NaT           8.0
2013-09-01    NaN
2013-09-06    NaN
2013-09-11    NaN
2013-09-16    NaN
2013-09-21    NaN
2013-09-26    3.0
2013-10-01    NaN
Name: Quantity, dtype: float64

In [11]: gb["Quantity"].aggregate(lambda values: np.nanmean(values))
Out[11]:
Date
2013-09-01    8.0
2013-09-06    NaN
2013-09-11    NaN
2013-09-16    NaN
2013-09-21    NaN
2013-09-26    NaN
2013-10-01    3.0
Name: Quantity, dtype: float64
``` Thanks these examples are useful.  I'll use them as test cases in the next PR.  Any objections to this PR as it stands?