BUG: inconsistent behaviour of Groupby (probably a regression)
First, thank you very much for your work ! I love pandas ! 

The problem
#### Code Sample

```python
import pandas as pd
# intialise data of lists. 
data = {'Fruit':['Apple', 'Orange', 'Apple'],
        'Origin':['France', 'France', 'Spain'],
        'Price':[10, 15, 20]}
  
# Create DataFrame without categorical variable
df = pd.DataFrame(data)
# Same dataframe with categorical variable
df_category = df.copy()
df_category['Origin'] = df_category['Origin'].astype('category')
df

# Normal behavior
df.groupby(['Fruit','Origin'])['Price'].mean()

# Abnormal behavior
df_category.groupby(['Fruit','Origin'])['Price'].mean()
```
#### Return
```python
# Normal behavior
Fruit   Origin
Apple   France    10
        Spain     20
Orange  France    15
Name: Price, dtype: int64

# Abnormal behavior
Fruit   Origin
Apple   France    10.0
        Spain     20.0
Orange  France    15.0
        Spain      NaN
Name: Price, dtype: float64
```
#### Problem description

Bug that should have been solved with #20583. I supposed it is a regression. No new discussion since May 2018, so I prefer to reopen a issue.

Brief description: When a column of a groupby is a categorical variable, the output include rows with NA.

#### Expected Output
```
Fruit   Origin
Apple   France    10
        Spain     20
Orange  France    15
Name: Price, dtype: int64
```
#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.0.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : fr_FR.UTF-8
LOCALE           : fr_FR.UTF-8

pandas           : 0.25.1
numpy            : 1.16.5
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.2
setuptools       : 41.0.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : None
pymysql          : None
psycopg2         : 2.7.5 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

</details>
