Masked arrays with hard masks don't read as missing
#### Code Sample, a copy-pastable example if possible

```python
arr = np.ma.array([1.0, 2.0], mask=[True, False], dtype='float64')
arr_hard = np.ma.array([1.0, 2.0], mask=[True, False], dtype='float64', hard_mask=True)
print(pd.DataFrame(arr))
print(pd.DataFrame(arr_hard))
```

Normal masked array:

| | 0 |
|:---|---:|
|0 | **NaN**|
|1 |2.0|

Hard masked array:

| | 0 |
|:---|---:|
|0 | **1.0**|
|1 |2.0|

#### Problem description

I expected the output for a "hard" masked array to be the same as a regular masked array – I thought both would give NaN for the masked value. If the two cases behaved the same, it would be less surprising.

The issue is the same for `Series` as well as `DataFrame`, as well as other types of float.

#### Expected Output


Normal masked array: no change

Hard masked array:

| | 0 |
|:---|---:|
|0 | **NaN**|
|1 |2.0|

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

<details>


INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-13-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
