BUG: "min_period" and "min_periods" problem in df.groupby().rolling
- [y] I have checked that this issue has not already been reported.

- [y] 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
# Your code here

import pandas as pd
name_l = ["Alice"] * 5 + ["Bob"] * 5 
val_l = [np.nan,np.nan,1,2,3]+[np.nan,1,2,3,4]
test_df = pd.DataFrame([name_l,val_l]).T
test_df.columns = ["name","val"]

# correct one
test_df.groupby("name")["val"].rolling(window=2,min_periods=1).sum()

# wrong one with "min_period” parameter 
test_df.groupby("name")["val"].rolling(window=2,min_period=1).sum() 

```

#### Problem description
The one with min_period does not work the same as min_periods and should not be allowed.

[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]

#### Expected Output
The one with min_period as parameter should produce an error.
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.0-7629-generic
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            : 1.16.4
pytz             : 2019.2
dateutil         : 2.7.3
pip              : 20.1
setuptools       : 46.1.3
Cython           : None
pytest           : 5.4.1
hypothesis       : None
sphinx           : 2.2.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.2
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10
IPython          : 7.11.1
pandas_datareader: None
bs4              : 4.8.2
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.2
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.1
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.12
tables           : None
tabulate         : None
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : 0.46.0

</details>
