.loc with date string and timestamp behave differently
#### Code Sample, a copy-pastable example if possible

```python
>>> import pandas as pd
>>> t0 = pd.Timestamp('2010-01-01')
>>> t1 = pd.Timestamp('2012-02-02')
>>> df = pd.DataFrame([1, 2, 3], index=pd.MultiIndex.from_tuples([(t0, 'A'), (t0, 'B'), (t1, 'A')]), columns=['C'])

>>> # Compare the following results:
>>> print(df.ix['2010-01-01'])
#    C
# A  1
# B  2
>>> print(df.loc['2010-01-01'])
#               C
# 2010-01-01 A  1
#            B  2
>>> print(df.ix[t0])
#    C
# A  1
# B  2
>>> print(df.loc[t0])
#    C
# A  1
# B  2
```

#### Problem description

The result of `df.loc['2010-01-01']` is different from that of `df.ix['2010-01-01']` or `df.loc[pd.Timestamp('2010-01-01')]`; it contains additional index level for date. (`df.ix[]` returns the same data frame for date string and timestamp slicer.)

#### Expected Output

```
----
   C
A  1
B  2
----
   C
A  1
B  2
----
   C
A  1
B  2
----
   C
A  1
B  2
----
```

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2+0.g825876c.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
