BUG: different apply function behavior when columns with type Timestamp present

[xvwei1989]

Code Sample, a copy-pastable example if possible

# Your code here
import pandas as pd
df = pd.DataFrame([[1,2],[1,2]],columns=['a','b'])
print df.apply(lambda x: {'s':x['a']+x['b']},1)
################
# (AS EXPECTED)
# output: 
# 0    {u's': 3}
# 1    {u's': 3}
# dtype: object
################

# add one new column with type Timestamp
df['tm'] = [pd.Timestamp('2017-05-01 00:00:00'),pd.Timestamp('2017-05-02 00:00:00')]
print df.apply(lambda x: {'s':x['a']+x['b']},1)

################
#(WRONG OUTPUT)
# output: 
#       a  b   tm
# 0   NaN NaN NaN
# 1   NaN NaN NaN
################

Problem description

when the return type of apply function is dict, if a new column with type Timestamp is added to the dataframe, the result will be unexpected even if the apply function is unchanged

Output of pd.show_versions()

commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: zh_CN.UTF-8
LOCALE: None.None

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.0.1
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 5.4.1
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.6
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: 1.1.14
pymysql: 0.7.11.None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jreback]

duplicate of #16353 and #15628

.apply infers the output dimension based on what you are returning, which looks exactly like a Series. This is not idiomatic pandas, not to mention non-performant.