BUG: query uses numexpr even when 'compute.use_numexpr' is False
- [ ] closes #32556
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry

If i understand correctly, the issue #32556 is about df.query does not care about the pd.set_option option, not about the extension dtype (which is still open at #25369)
@teto used ```pd.set_option('compute.use_numexpr', False)``` as a workaround for the original dtype issue but turned out it is also a bug on its own. @quangngd  that's an accurate description of the issue. Thanks for taking care of this ! @quangngd you need to have a test which fails on master but works here. can you construct one. there is one in my issue (might not be usable out of the box though). @teto I don't think it's a good idea using a bug as a test as it would be fixed in the future. So to my understanding, the test must be able to differentiate the used engine during the ```df.query```/ ```df.eval``` call (yah, just found out the bug is in ```df.eval``` too, and is too fixed by this pr). The thing is we could't use any "non-parse-able"  behaviour since it could be a bug and could be fixed in the future.
The best I could come up with is the following based on [numexpr' note](https://numexpr.readthedocs.io/en/latest/user_guide.html#notes).
```py
import pandas as pd
df = pd.DataFrame(data=[4+1j,5,6], columns=["col_test"])
s = "abs(col_test)"
display(df.eval(s, engine='python').dtype)
display(df.eval(s, engine='numexpr').dtype)
```
output:
```py
dtype('float64')
dtype('complex128')
```
But so far I can't think of how to make a test for ```df.query``` @quangngd I am not sure of the status here. please merge master and address comments. @jreback the fix is already there but I cannot think of a sensible test @quangngd: What about this?

    from pandas.core.computation.eval import _check_engine
    with pd.option_context('compute.use_numexpr', False):
        result = _check_engine(None)
        assert result == 'python' @rhshadrach thankyou, the test indeed fails on master and pass on my branch. Where should be the proper place to place the test? > Where should be the proper place to place the test?

`tests.computation.test_eval` closing, this needs a reproducible tests that fails before any patch. (it can certainly be part of the patch, but demonstrate on the issue) wait what ? it's not easy to reproduce, you would need to disable one package or monkeypatch one package I suppose. The absence of a test should not prevent fixing an issue/document it/remove the feature. w/o a test how do you know it was fixed? Is this test not sufficient?

https://github.com/pandas-dev/pandas/pull/33006#issuecomment-654424734 sure if it fails on master 