BUG: creating dataframe from masked numpy array turns missing string values into NaN

[mvashishtha]

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
import numpy as np
import numpy.ma as ma

x = np.array(['a', 'b'])
mx = ma.masked_array(x, mask=[0, 1])
print(pd.DataFrame(mx))

Issue Description

creating dataframe from masked numpy array turns missing string values into NaN

Expected Behavior

For examples like this, where the masked array is of string dtype, I expect pandas to fill missing data with None instead of NaN.

Installed Versions

Details

INSTALLED VERSIONS
------------------
commit           : 0f437949513225922d851e9581723d82120684a6
python           : 3.8.16.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 22.5.0
Version          : Darwin Kernel Version 22.5.0: Mon Apr 24 20:51:50 PDT 2023; root:xnu-8796.121.2~5/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 2.0.3
numpy            : 1.24.4
pytz             : 2023.3
dateutil         : 2.8.2
setuptools       : 67.8.0
pip              : 23.1.2
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : 8.12.2
pandas_datareader: None
bs4              : None
bottleneck       : None
brotli           : None
fastparquet      : None
fsspec           : None
gcsfs            : None
matplotlib       : None
numba            : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyreadstat       : None
pyxlsb           : None
s3fs             : None
scipy            : None
snappy           : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
zstandard        : None
tzdata           : 2023.3
qtpy             : None
pyqt5            : None

[mvashishtha]

Possibly #51872 and #20427 are relevant.

[phofl]

Agreed, Duplicate of #51872