BUG: Regression on np.logical_or, it behaves erraticly from 2.0, breaking backwards compatibility

[PabloRuizCuevas]

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Reproducible Example

import numpy as np
import pandas as pd
a = pd.DataFrame({"or":[False,True], "a":[True,True], "b":[True, False]})

# fails with:  ValueError: cannot reindex on an axis with duplicate labels
np.logical_or(a[["a","b"]], a[2*["or"]])

# Erratic unpredictable behaviour with no fail :(
np.logical_or(a[["a","b"]], a[["or","a"]])

Issue Description

This used to work in pandas 1.x as a element wise logical operation, now is not working anymore. Even worst in some cases may be completely overlooked as it doesn't raise errors but another df with mixed nans and booleans

Expected Behavior

logical element wise operation that returns two colums with booleans

Installed Versions

INSTALLED VERSIONS

commit : 0f43794
python : 3.11.2.final.0
python-bits : 64
OS : Linux
OS-release : 6.2.0-20-generic
Version : #20-Ubuntu SMP PREEMPT_DYNAMIC Thu Apr 6 07:48:48 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

[PabloRuizCuevas]

More info about this error can be found at:

Probably the cause:
#42568

Seems related, but not sure:
#37374

May be the code that causes it
https://github.com/pandas-dev/pandas/blob/main/pandas/core/generic.py#L2099

SO
https://stackoverflow.com/questions/76838487/are-numpy-logical-element-wise-operations-broken-for-pandas-2-0-np-logical-or/76838688#76838688