BUG: Creating a DataFrame from a structured masked NumPy array.
- [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.

---

**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

```python
import numpy as np
import pandas as pd
import numpy.ma as ma

# create a masked, structured array
a = np.ma.array([(1, 2.2), (42, 5.5)],
                dtype=[('a',int),('b',float)],
                mask=[(True,False),(False,True)])

b = pd.DataFrame(a)
```

#### Problem description

This is somewhere between a bug report and a feature request? The above code returns this error:

```python
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-a4c5236a3cd4> in <module>
----> 1 pd.DataFrame(a)

/usr/local/anaconda/lib/python3.8/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    636             # a masked array
    637             else:
--> 638                 data = sanitize_masked_array(data)
    639                 mgr = ndarray_to_mgr(
    640                     data,

/usr/local/anaconda/lib/python3.8/site-packages/pandas/core/construction.py in sanitize_masked_array(data)
    452     """
    453     mask = ma.getmaskarray(data)
--> 454     if mask.any():
    455         data, fill_value = maybe_upcast(data, copy=True)
    456         data.soften_mask()  # set hardmask False if it was True

/usr/local/anaconda/lib/python3.8/site-packages/numpy/core/_methods.py in _any(a, axis, dtype, out, keepdims, where)
     54     # Parsing keyword arguments is currently fairly slow, so avoid it for now
     55     if where is True:
---> 56         return umr_any(a, axis, dtype, out, keepdims)
     57     return umr_any(a, axis, dtype, out, keepdims, where=where)
     58 

TypeError: cannot perform reduce with flexible type
```

The [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=masked%20array#from-a-list-of-dataclasses) notes that:

> Alternatively, you may pass a numpy.MaskedArray as the data argument to the DataFrame constructor, and its masked entries will be considered missing.

The above works if `dtype` is omitted; all values are "upgraded" to floats.

#### Expected Output

Several possibilities:

* This is expected behavior. If so, it would be nice to have the documentation linked above note that masked structured NumPy arrays are not supported. The code should also raise the appropriate exception informing the user these arrays are not supported (e.g. `ValueError`?).
* This functionality is supported. If so, this appears to be a bug.
* Functionality is supported, but with extra steps. It seems as though this conversion is possible with these steps.
    * Use [numpy.ma.MaskedArray.filled](https://numpy.org/devdocs/reference/generated/numpy.ma.MaskedArray.filled.html) and fill the NumPy masked array with "special" (e.g. 1e20, 99999) values based on data type.
    * Convert the array to a `DataFrame`.
    * Convert any integer series to the nullable int type `Int64`.
    * Replace the same filled values to appropriate NULL values, e.g. `NaN`, `pd.NA`.

Options to address the latter case can be either supported in code or else documentation.

This issue was discussed on StackOverflow [here](https://stackoverflow.com/questions/68532200/creating-a-pandas-dataframe-from-a-numpy-masked-array/68534675#68534675) (my question).

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : f00ed8f47020034e752baf0250483053340971b0
python           : 3.8.8.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.6.0
Version          : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:31 PDT 2021; root:xnu-7195.141.2~5/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.0
numpy            : 1.20.3
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.1.3
setuptools       : 52.0.0.post20210125
Cython           : 0.29.24
pytest           : 6.2.4
hypothesis       : None
sphinx           : 4.0.2
blosc            : None
feather          : None
xlsxwriter       : 1.4.4
lxml.etree       : 4.6.3
html5lib         : 1.1
pymysql          : None
psycopg2         : 2.8.6 (dt dec pq3 ext lo64)
jinja2           : 3.0.1
IPython          : 7.22.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 2021.07.0
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.6.2
sqlalchemy       : 1.4.21
tables           : 3.6.1
tabulate         : None
xarray           : 0.18.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.53.0

</details>
