DataFrame.apply returns NaN if DataFrame contains datetime column
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

A = pd.DataFrame()
A["author"] = ["X", "Y", "Z"]
A["publisher"] = ["BBC", "NBC", "N24"]
A["date"] = pd.to_datetime(['17-10-2010 07:15:30', '13-05-2011 08:20:35', "15-01-2013 09:09:09"])

# the following produces the faulty result
A.apply(lambda x: {}, axis=1)
```

#### Problem description

The last line returns a dataframe with _all_ entries replaced by NaN. This only happens if the following two conditions are _both_ satisfied:
- a column with `datetime64[ns]` is present in the dataframe (in the above example the column with name `date`)
- the function applied to the dataframe returns a dictionary
When using a Dataframe without the datetime column, the code returns the expected result (for the above result a `pd.Series` with empty dictionaries). 

**Why this is a (significant) problem:** 
Output of apply depends on presence of another column that is not used by applied function.

**Potentially related:**
I tried to search for a similar issues and found the already closed issues
- [#8735](https://github.com/pandas-dev/pandas/issues/8735)
- [#10740](https://github.com/pandas-dev/pandas/pull/10740)

However, these issues are fixed and already closed since 2015.


#### Expected Output
the expected output can be easily produced by removing the 6th line (`A["date"] = ...`)
```
>>> A.apply(lambda x: {}, axis=1)
0    {}
1    {}
2    {}
dtype: object
```


#### Output of ``pd.show_versions()``
Checked with newest version of pandas: 
- pandas (0.21.1)
- numpy (1.13.3)
- Python 3.6.2

[paste the output of ``pd.show_versions()`` here below this line]

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Darwin
OS-release: 17.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_CA.UTF-8
pandas: 0.21.1
pytest: None
pip: 9.0.1
setuptools: 36.6.0
Cython: None
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
```
</details>
