BUG: read_excel with dtype=np.int32 can't handle empty or nan cells
- [x] I have checked that this issue has not already been reported.
Though there's some similar issues, like https://github.com/pandas-dev/pandas/issues/20377

- [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

**EDIT:**
(won't let me comment)
Apparently, it's possible with `dtype='Int64'` (capital `I` and in ticks).
https://pandas.pydata.org/pandas-docs/version/0.24/whatsnew/v0.24.0.html#optional-integer-na-support
Unfortunately, that's not very well documented. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html makes it sound like you're supposed to give it a numpy type.
So it's more of a documentation issue, instead of a bug
**/EDIT**

```python
# save the lines below as test.xls:

ID  string
1   a
2   b
3   c
    d
4   e
nan f
6   g

# code:
test = pd.read_excel('test.xls')
test
    ID string
0  1.0      a
1  2.0      b
2  3.0      c
3  NaN      d
4  4.0      e
5  NaN      f
6  6.0      g

# note that the IDs gained a decimal! They're needed as an ID to retrive some files, to that 0 is breaking things. So let's import `ID` as integer:

test = pd.read_excel('test.xls', dtype={'ID':np.int32})        

ValueError                                Traceback (most recent call last)
...
ValueError: Cannot convert non-finite values (NA or inf) to integer
...
ValueError: Unable to convert column ID to type <class 'numpy.int32'>

# similar things happen with the nan cell.
```

#### Problem description

Importing an excel file with `nan` or ` ` in a column of integers fails, when specifying this column as `dtype=np.int32`

#### Expected Output

setting `nan` or ` ` as `np.nan` or similar or having a documented workaround in the documentation. `na_filter=False` seems promising, but doesn't do anything.

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

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.18.0-147.8.1.el8_1.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 47.1.1.post20200604
Cython           : 0.29.17
pytest           : 5.4.2
hypothesis       : 5.5.4
sphinx           : 3.0.4
blosc            : None
feather          : None
xlsxwriter       : 1.2.8
lxml.etree       : 4.5.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.13.0
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.1.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.2
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.17
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.8
numba            : 0.49.1


</details>
