DOC: DataFrameGroupBy.diff() returns no columns used for grouping
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this bug exists on the master branch of pandas.


### Reproducible Example

```python
import pandas as pd
dates = pd.date_range("2020-01-01", "2020-01-02").values
availability = pd.DataFrame({'product': ['a', 'a', 'b', 'b'],
                             'timestamp': np.append(dates, dates),
                             'stock': [10, 5, 10, 10]
                             })
(availability.set_index('timestamp')
 .groupby('product').diff()
 )
```


### Issue Description

Using with `groupby(grouping_columns).diff()`  leads to `grouping_columns` columns being missing in the output.

### Expected Behavior

Expected behaviour: having 'product' in the index.
```
                    stock
product timestamp        
a       2020-01-01    NaN
        2020-01-02   -5.0
b       2020-01-01    NaN
        2020-01-02    0.0
```




### Installed Versions

INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.8.10.final.0

pandas           : 1.3.4
numpy            : 1.20.3
