BUG when filtering dates "less than or equal to" with HOUR in datetime64[ns] columns
Please, consider the following code:

```
    In [0]: import pandas as pd
    In [1]: my_data = [{'TICKET_NUMBER' : '001', 'DATE' : '2015-11-11 11:15'},
                      {'TICKET_NUMBER' : '002', 'DATE' : '2015-11-12 14:32'},
                      {'TICKET_NUMBER' : '003', 'DATE' : '2015-11-13 15:33'},
                      {'TICKET_NUMBER' : '004', 'DATE' : '2015-11-14 11:34'},
                      {'TICKET_NUMBER' : '005', 'DATE' : '2015-11-15 17:35'},
                      {'TICKET_NUMBER' : '006', 'DATE' : '2015-11-16 19:36'},
                      {'TICKET_NUMBER' : '007', 'DATE' : '2015-11-17 12:37'},
                      {'TICKET_NUMBER' : '008', 'DATE' : '2015-11-18 15:38'}]

    In [2]: df
    Out [2]:          DATE                  TICKET_NUMBER
                 0    2015-11-11 11:15      001
                 1    2015-11-12 14:32      002
                 2    2015-11-13 15:33      003
                 3    2015-11-14 11:34      004
                 4    2015-11-15 17:35      005
                 5    2015-11-11 11:15      006
                 6    2015-11-16 19:36      007
                 7    2015-11-18 15:38      008

    In [3]: df = pd.DataFrame(my_data)
    In [4]: df.DATE = pd.to_datetime(df.DATE)
    In [5]: df[df.DATE<='2015-11-13']
    Out [5]:          DATE                  TICKET_NUMBER
                 0    2015-11-11 11:15      001
                 1    2015-11-12 14:32      002
```

The above code should print rows with dates "LESS THAN OR EQUAL TO" 2015-11-13 (indexes 0,1 AND 2), but it only shows rows with dates "LESS THAN" 2015-11-13 (that is, indexes 0 and 1, not 3).

Remarks:

(1) Filtering "MORE THAN OR EQUAL TO" some date (instead of LESS) works as expected.

(2) Removing the HOUR in the DATE makes everything work fine. This works as expected:

```
    In [1]: my_data = [{'TICKET_NUMBER' : '001', 'DATE' : '2015-11-11'},
                      {'TICKET_NUMBER' : '002', 'DATE' : '2015-11-12'},
                      {'TICKET_NUMBER' : '003', 'DATE' : '2015-11-13'},
                      {'TICKET_NUMBER' : '004', 'DATE' : '2015-11-14'},
                      {'TICKET_NUMBER' : '005', 'DATE' : '2015-11-15'},
                      {'TICKET_NUMBER' : '006', 'DATE' : '2015-11-16'},
                      {'TICKET_NUMBER' : '007', 'DATE' : '2015-11-17'},
                      {'TICKET_NUMBER' : '008', 'DATE' : '2015-11-18'}]
```

(3) Setting the HOUR to 00:00 also makes it work as expected.

(4) pd.installed_versions()

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
LANG: es_ES.UTF-8

pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.3
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
