BUG: DataFrame.stack does not work when columns includes tuple level

[or-jether]

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

df = pd.DataFrame(
    index=[1, 2],
    columns=pd.MultiIndex.from_tuples(
        [((1, 2), 'a'), ((3, 4), 'b')], names=['level_0', 'level_1']
    ),
    data=[[1, 2], [3, 4]],
)
df.stack() # This raises ValueError: Names should be list-like for a MultiIndex

Issue Description

Using DataFrame.stack on a dataframe that includes tuple in one of the column levels (stack the non-tuple level) results in an exception. This seems to only happen if the level has a name.

Expected Behavior

We should get something like this:
df.T.unstack().T

     (1, 2)  (3, 4)
1 a     1.0     NaN
  b     NaN     2.0
2 a     3.0     NaN
  b     NaN     4.0

Installed Versions

INSTALLED VERSIONS

commit : 37ea63d
python : 3.10.12.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 2.0.1
numpy : 1.24.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 57.5.0
pip : 22.3.1
Cython : 3.0.8
pytest : 7.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.1.2
lxml.etree : 5.1.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.15.0
pandas_datareader: None
bs4 : 4.12.2
bottleneck : 1.3.7
brotli : None
fastparquet : None
fsspec : 2023.9.0
gcsfs : 2023.9.0
matplotlib : 3.7.2
numba : 0.59.0
numexpr : 2.8.4
odfpy : None
openpyxl : 3.1.2
pandas_gbq : 0.19.2
pyarrow : 12.0.1
pyreadstat : None
pyxlsb : 1.0.10
s3fs : None
scipy : 1.10.1
snappy : 0.7.1
sqlalchemy : None
tables : 3.8.0
tabulate : None
xarray : 2023.8.0
xlrd : 2.0.1
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

[rhshadrach]

I cannot reproduce on main - @or-jether can you try using the most recent version of pandas (2.2.2) with future_stack=True.