BUG: edge case when masking "null[pyarrow]" pd.Series

[lukaszkolodziejczyk]

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
pd.Series([pd.NA], dtype="null[pyarrow]").mask([True], pd.NA)

Issue Description

It fails in the following way:

/Users/runner/work/crossbow/crossbow/arrow/cpp/src/arrow/result.cc:28: Constructed with a non-error status: OK

Process finished with exit code 134 (interrupted by signal 6:SIGABRT)

Expected Behavior

I'd expect it to return:
pd.Series([pd.NA], dtype="null[pyarrow]")

Installed Versions

INSTALLED VERSIONS ------------------ commit : d9cdd2e python : 3.11.0.final.0 python-bits : 64 OS : Darwin OS-release : 23.1.0 Version : Darwin Kernel Version 23.1.0: Mon Oct 9 21:27:24 PDT 2023; root:xnu-10002.41.9~6/RELEASE_ARM64_T6000 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 69.5.1
pip : 24.0
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 : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 16.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[rob-sil]

Could this be an upstream bug in PyArrow? I get the same error when running the no-pandas version:

import pyarrow as pa
import pyarrow.compute as pc

mask = pa.array([True])
values = pa.array([None])
pc.replace_with_mask(values, mask, pa.NA)