title: pandas merge on date column issue
body: <p>I am trying to merge two dataframes on date column (tried both as type <code>object</code> or <code>datetime.date</code>, but fails to give desired merge output:</p>
  
  <pre><code>import pandas as pd
  df1 =  pd.DataFrame({'amt': {0: 1549367.9496070854,
        1: 2175801.78219801,
        2: 1915613.1629125737,
        3: 1703063.8323954903,
        4: 1770040.7987461537},
       'month': {0: '2015-02-01',
        1: '2015-03-01',
        2: '2015-04-01',
        3: '2015-05-01',
        4: '2015-06-01'}})
  print(df1)
  
  
          amt             month
      0   1.549368e+06    2015-02-01
      1   2.175802e+06    2015-03-01
      2   1.915613e+06    2015-04-01
      3   1.703064e+06    2015-05-01
      4   1.770041e+06    2015-06-01
  
  
  
  df2 =  {'factor': {datetime.date(2015, 2, 1): 1.0,
        datetime.date(2015, 3, 1): 1.0,
        datetime.date(2015, 4, 1): 1.0,
        datetime.date(2015, 5, 1): 1.0,
        datetime.date(2015, 6, 1): 0.99889679025914435},
       'month': {datetime.date(2015, 2, 1): datetime.date(2015, 2, 1),
        datetime.date(2015, 3, 1): datetime.date(2015, 3, 1),
        datetime.date(2015, 4, 1): datetime.date(2015, 4, 1),
        datetime.date(2015, 5, 1): datetime.date(2015, 5, 1),
        datetime.date(2015, 6, 1): datetime.date(2015, 6, 1)}}
  df2 = pd.DataFrame(df2)
  print(df2)
  
                  factor      month
      2015-02-01  1.000000    2015-02-01
      2015-03-01  1.000000    2015-03-01
      2015-04-01  1.000000    2015-04-01
      2015-05-01  1.000000    2015-05-01
      2015-06-01  0.998897    2015-06-01
  
  
  pd.merge(df2, df1, how='outer', on='month')
  
          factor       month            amt
      0   1.000000     2015-02-01      NaN
      1   1.000000     2015-03-01      NaN
      2   1.000000     2015-04-01      NaN
      3   1.000000     2015-05-01      NaN
      4   0.998897     2015-06-01      NaN
      5   NaN           2015-02-01    1.549368e+06
      6   NaN           2015-03-01    2.175802e+06
      7   NaN           2015-04-01    1.915613e+06
      8   NaN           2015-05-01    1.703064e+06
      9   NaN           2015-06-01    1.770041e+06
  </code></pre>