[Bug][Regression] df.groupby.apply fails under specific conditions
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import datetime

def get_vals(x):
    return pd.Series([0,1,2], index=[2000, 2001, 2002])

b = list(range(0,3))*2
y = list(range(2000,2003))*2
df = pd.DataFrame({'b':b,'y':y}) 
df['date'] = pd.to_datetime(df['y'].apply(lambda x: datetime.date(x, 1, 1)))


print(pd.__version__)

print(df)

df.groupby(['b']).apply(lambda x: get_vals(x))

```
#### Problem description

The above code gives an error (KeyError: 0) in pandas 0.25.1, while it runs as expected in pandas 0.24.2

The apply function returns a timeseries.
I found that triggering the error is related to having a datetime column in the dataframe. Without that column it does not throw an error. 

#### Expected Output
No error and the following output, as can be verified with pandas version 0.24.2:
![image](https://user-images.githubusercontent.com/7702207/64076125-f6ec9e00-ccc0-11e9-9f6f-53e88a1ee980.png)

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
Environment of 0.25.1 that I tested on:

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.16.4
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       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : None
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.7
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None

Environment of 0.24.2 that I tested on:
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.2
pytest: 5.1.1
pip: 19.2.3
setuptools: 41.2.0
Cython: 0.29.13
numpy: 1.16.4
scipy: 1.3.1
pyarrow: None
xarray: None
IPython: 7.7.0
sphinx: 2.2.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.7.0
feather: None
matplotlib: 3.1.1
openpyxl: 2.6.3
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.2.0
lxml.etree: 4.4.1
bs4: 4.8.0
html5lib: 1.0.1
sqlalchemy: 1.3.7
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
