title: asfreq yields unexpected results with Period dtype
body: <p>When upsampling a Dataframe, I would to like that new rows created are left empty.</p>
  
  <p>Considering following code:</p>
  
  <pre><code>import pandas as pd
  
  p5h = pd.period_range(start='2020-02-01 00:00', end='2020-03-04 00:00', freq='5h', name='p5h')
  
  df = pd.DataFrame({'Values' : 1}, index=p5h)
  </code></pre>
  
  <p>I would like to upsample to '1H' frequency, leaving new rows filled with NaN values.</p>
  
  <pre><code>import numpy as np
  
  df1h = df.asfreq('1H', method=None, how='start', fill_value = np.NaN)
  </code></pre>
  
  <p>But here is what I get:</p>
  
  <pre><code> df1h.head(7)
  
                     Values
   p5h                     
   2020-02-01 00:00       1
   2020-02-01 05:00       1
   2020-02-01 10:00       1
   2020-02-01 15:00       1
   2020-02-01 20:00       1
   2020-02-02 01:00       1
   2020-02-02 06:00       1
  </code></pre>
  
  <p>(need for that is then to merge/join/concat this DataFrame to another one having a '1H' PeriodIndex - this merging operation cannot be achieved if PeriodIndex of both DataFrames do not share the same frequency)</p>
  
  <p>Thanks for any help! Bests</p>