CLN: Cleanups in groupby due to numeric_only deprecations
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [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
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Summary:

 - For ops that call `_op_via_apply`, numeric_only being None or lib.no_default is the same as False since these just call the corresponding DataFrame methods where the deprecation has already been enforced
 - The above remark also applies for idxmin/idxmax
 - Where we set `ignore_failures=numeric_only is lib.no_default`, across groupby numeric_only=lib.no_defualt is internally treated as numeric_only=True, so there is no reason to ignore failures.
 - In the one test change, this is where groupby attempts to default to `numeric_only=True` but as this results in an empty frame the op instead defaults to `numeric_only=False`. For the whatsnew, this will be covered by the general message that groupby will no longer automatically drop nuisance columns.
Thanks @rhshadrach 