Resampling on index and column after groupby give different results
#### Code Sample, a copy-pastable example if possible

This should be copy-pastable and self-contained:
```python
import pandas as pd

get_df = lambda : pd.DataFrame( {'DATETIME' : pd.to_datetime(['2018-01-01 11:25:00', '2018-01-01 11:50:00', '2018-01-03 10:30:00'
                                                    , '2018-01-04 10:25:00']*2),
                                 'GROUP'    : ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                                 'FILTER'   : [True, True, True, True, False, False, True, True],
                                 'X'        : [1, 2, 3, 4, 5, 6, 7, 8]} )

df = get_df()
df = df.set_index('DATETIME')
df.groupby('GROUP').resample('D').X.sum()                               # <-- LINE A
# Returns
# -------
# GROUP  DATETIME  
# A      2018-01-01     3
#        2018-01-02     0
#        2018-01-03     3
#        2018-01-04     4
# B      2018-01-01    11
#        2018-01-02     0
#        2018-01-03     7
#        2018-01-04     8
# Name: X, dtype: int64

df = get_df()
df.groupby('GROUP').resample('D', on = 'DATETIME').X.sum()               # <-- LINE B
# Returns
# -------
# GROUP  DATETIME  
# A      2018-01-01    10
# B      2018-01-03    11
#        2018-01-04    15
# Name: X, dtype: int64

df = get_df()
df = df.set_index('DATETIME')
df[df.FILTER].groupby('GROUP').resample('D').X.sum()                     # <-- LINE C
# Returns
# -------
# GROUP  DATETIME  
# A      2018-01-01    3
#        2018-01-02    0
#        2018-01-03    3
#        2018-01-04    4
# B      2018-01-03    7
#        2018-01-04    8
# Name: X, dtype: int64

df = get_df()
df[df.FILTER].groupby('GROUP').resample('D', on = 'DATETIME').X.sum()   # <-- LINE D
# Error
# -----
#    IndexError: index 6 is out of bounds for size 6

```
#### Problem description

Lines A and B are identical except that one does a resample on an index, and one does it on an identical column.

Lines C and D are identical except that one does a resample on an index, and one does it on an identical column.

Yet, they behave differently!

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.2.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.3
numpy            : 1.16.2
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : 0.29.12
pytest           : 5.0.1
hypothesis       : None
sphinx           : 2.1.2
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : 4.3.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.6.1
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.0
matplotlib       : 3.0.2
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : 1.3.5
tables           : 3.4.4
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.8

</details>

Thanks!!