BUG: The behavior of DataFrame.corrwith against `Series` is changed?

[itholic]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

df_bool = pd.DataFrame({"A": [True, True, False, False], "B": [True, False, False, True]})
ser_bool = pd.Series([True, True, False, True])

df_bool.corrwith(ser_bool)

Issue Description

In pandas < 1.5.0:

>>> df_bool.corrwith(ser_bool)
B    0.57735
A    0.57735
dtype: float64

In pandas 1.5.0:

>>> df_bool.corrwith(ser_bool)
Series([], dtype: float64)

So, I wonder this behavior change is intended, or a bug ?

Expected Behavior

I expected the result is the same as pandas < 1.5.0 as below:

>>> df_bool.corrwith(ser_bool)
B    0.57735
A    0.57735
dtype: float64

rather than returning the empty Series.

Installed Versions

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.9.11.final.0
python-bits : 64
OS : Darwin
OS-release : 21.6.0
Version : Darwin Kernel Version 21.6.0: Wed Aug 10 14:25:27 PDT 2022; root:xnu-8020.141.5~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8

pandas : 1.5.0
numpy : 1.22.3
pytz : 2021.3
dateutil : 2.8.2
setuptools : 61.2.0
pip : 22.2.2
Cython : 0.29.24
pytest : 6.2.5
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.29.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.4
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : 2.8.1
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 9.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.2
snappy : None
sqlalchemy : 1.4.26
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

[MarcoGorelli]

is this different from #48826 ? looks like it'd be solved by reverting the same PR

(pandas-dev) marcogorelli@DESKTOP-U8OKFP3:~/pandas-dev$ git branch --show-current 
revert-5efb570
(pandas-dev) marcogorelli@DESKTOP-U8OKFP3:~/pandas-dev$ . repl.sh 

In [1]: import pandas as pd
   ...: 
   ...: df_bool = pd.DataFrame({"A": [True, True, False, False], "B": [True, False, False, True]})
   ...: ser_bool = pd.Series([True, True, False, True])
   ...: 
   ...: df_bool.corrwith(ser_bool)
Out[1]: 
A    0.57735
B    0.57735
dtype: float64

[phofl]

No this is a duplicate, but we should add this to the other issue to get a test in