BUG: Groupby + Fillna loses the group
Hello there and happy new year!

Perhaps one of the last bugs of 2020... Please consider this example where I fill forward missing values by groups.
```

pd.__version__
Out[33]: '1.2.0'

df = pd.DataFrame({'group' : ['a','a','a','b','b'],
                   'time' : [pd.to_datetime('2020-01-01 10:00:00'),
                             pd.to_datetime('2020-01-01 10:00:01'),
                             pd.to_datetime('2020-01-01 10:00:02'),
                             pd.to_datetime('2020-01-01 10:00:00'),
                             pd.to_datetime('2020-01-01 10:00:01')],
                   'values' : [np.NaN,2,np.NaN,1,np.NaN]})

df.set_index('time', inplace = True)
df
Out[32]: 
                    group     values
time                                
2020-01-01 10:00:00     a        NaN
2020-01-01 10:00:01     a 2.00000000
2020-01-01 10:00:02     a        NaN
2020-01-01 10:00:00     b 1.00000000
2020-01-01 10:00:01     b        NaN
```

The folllowing used to work but now it loses the `group` var entirely!

```
df.groupby('group').fillna(method = 'ffill')
Out[34]: 
                        values
time                          
2020-01-01 10:00:00        NaN
2020-01-01 10:00:01 2.00000000
2020-01-01 10:00:02 2.00000000
2020-01-01 10:00:00 1.00000000
2020-01-01 10:00:01 1.00000000
```

```
df.groupby('group', as_index = False).fillna(method = 'ffill')
Out[35]: 
                        values
time                          
2020-01-01 10:00:00        NaN
2020-01-01 10:00:01 2.00000000
2020-01-01 10:00:02 2.00000000
2020-01-01 10:00:00 1.00000000
2020-01-01 10:00:01 1.00000000
```

Is this the expected behavior? I can still use `apply` as a workaround

```
df.groupby('group').apply(lambda x: x.fillna(method = 'ffill'))
Out[36]: 
                    group     values
time                                
2020-01-01 10:00:00     a        NaN
2020-01-01 10:00:01     a 2.00000000
2020-01-01 10:00:02     a 2.00000000
2020-01-01 10:00:00     b 1.00000000
2020-01-01 10:00:01     b 1.00000000
```

Thanks!