Bug: DataFrame.clip fixed for lower > upper
Fixed behavior of DataFrame.clip for `lower` > `upper` arguments. Changed to be consistent with numpy - all equal to `upper`. Main reason of bug was undocumented swap of arguments for scalars and for list-like arguments comparison with initial DataFrame for both bounds.

- [x] closes #52147 
- [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.

Looks like some tests are failing after merging in main branch. I will look at this. I personally think raising is the most informative behavior, but open to thoughts from other core team members It is worth mentioning that `Series.clip` also works in the same way as `numpy.clip`. I think it is rather logical result, in first step we are clipping lower numbers, in second step upper numbers. We end up with all numbers equal to `upper`.

Another similar example from pandas api, `pandas.date_range` - when we pass `start` > `end` there is no exception, but empty `DatetimeIndex`. > Another similar example from pandas api, pandas.date_range - when we pass start > end there is no exception, but empty DatetimeIndex.

`pd.date_range` makes sense to be more flexible in this sense because the `freq` can be negative allowing start > end to produce a non-empty date range Ok, so I guess we also need to change `Series.clip` method to raise exception. 

I am not sure which exception it should raise, some existing pandas exception? I guess `ValueError` might be appropriate. I have started implementation and I think there is too much to make validation for scalars, lists and DataFrame arguments. 

So if you insist on doing this, I can close this PR and unassign from issue. I would suggest closing this PR now until there is some consensus on the main issue on how to handle this scenario Thanks for the PR, but I think this scenario needs a little more discussion before moving forward so closing