BUG: series.replace() fails when string series contains pd.NA
- [x ] I have checked that this issue has not already been reported.

- [x ] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

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**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Code Sample, a copy-pastable example

```
def test_pandas_replace_na():
    series = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA], dtype="string")
    original = list(series)
    regex_mapping = {
        "AA": "CC",
        "BB": "CC",
        "EE": "CC",
        "CC": "CC-REPL",
        }
    series2 = series.replace(regex_mapping, regex=True)
    assert list(series2) == ["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA]
```

#### Problem description
The test case above causes this error in compare_or_regex_search():
ValueError: NumPy boolean array indexing assignment cannot assign 7 input values to the 6 output values where the mask is true
#### Expected Output
See test case.
Is there a suggested workaround?
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python           : 3.8.8.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.18362
machine          : AMD64
processor        : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : English_United States.1252

pandas           : 1.3.2
numpy            : 1.20.2
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.1.1
setuptools       : 49.6.0.post20210108
Cython           : None
pytest           : 6.2.4
hypothesis       : None
sphinx           : 4.1.2
blosc            : None
feather          : None
xlsxwriter       : 3.0.1
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.23.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.1
numexpr          : None
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.3
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.53.1

</details>
