ENH: Preserve Index in Pandas Groupby Rolling on Datetime 
#### Is your feature request related to a problem?

A problem arises when rolling on datetime column where dates are the same. The Groupby Rolling function does not preserve the original index and so when dates are the same within the Group, it is impossible to know which index value it pertains to from the original dataframe.

#### Describe the solution you'd like

Ideally we wish to preserve the original index so that it can be merged back onto the original dataframe.

#### Current Code

```python
# Your code here, if applicable
df = pd.DataFrame({"column1": range(6), 
                   "column2": range(6), 
                   'group': 3*['A','B'], 
                   'date':pd.date_range("20190101", periods=6)})
df.loc[:,'date']=df.loc[0,'date']

df.groupby('group').rolling('1D',on='date')['column1'].sum()

## Output
group  date      
A      2019-01-01    0.0
       2019-01-01    2.0
       2019-01-01    6.0
B      2019-01-01    1.0
       2019-01-01    4.0
       2019-01-01    9.0
Name: column1, dtype: float64
```

#### Describe alternatives you've considered

Current workaround as follows, but considerably slower
```
df.groupby('group').apply(lambda x: x.rolling('1D',on='date',closed='left')['column1'].sum())

## Output
group   
A      0    NaN
       2    0.0
       4    2.0
B      1    NaN
       3    1.0
       5    4.0
Name: column1, dtype: float64
```