Should I be able to initialise a Series with dtype PeriodDtype?
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

pd.Series([pd.PeriodIndex(['2000-03'], freq='M')])  # works
pd.Series([pd.PeriodIndex(['2000-03'], freq='M')], dtype=pd.PeriodDtype('M'))  # fails                                                                                                                                                                                                                                                                                                 
```
#### Problem description

The second command fails with
```
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```

This confused me, as I am able to initialise Series with other dtypes:
```
pd.Series([pd.Interval(left=0, right=5)], dtype=pd.IntervalDtype())  # works
pd.Series([1], dtype=pd.Int64Dtype())  # works
pd.Series([1], dtype=pd.CategoricalDtype())  # works
```

#### Expected Output

Probably something like this
```
0    PeriodIndex(['2000-03'], dtype='period[M]', fr...
dtype: 'period[M]'
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : 2ec41bdc9b6dd59f48249262aa46e384698aa648
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-72-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 0.25.0+1471.g2ec41bdc9.dirty
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.3.1
setuptools       : 42.0.2.post20191201
Cython           : 0.29.14
pytest           : 5.3.2
hypothesis       : 4.55.4
sphinx           : 2.3.0
blosc            : None
feather          : None
xlsxwriter       : 1.2.6
lxml.etree       : 4.4.2
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : 1.3.1
fastparquet      : 0.3.2
gcsfs            : None
lxml.etree       : 4.4.2
matplotlib       : 3.1.2
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.1
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
pytest           : 5.3.2
s3fs             : 0.4.0
scipy            : 1.4.0
sqlalchemy       : 1.3.11
tables           : 3.6.1
tabulate         : 0.8.6
xarray           : 0.14.1
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.6
numba            : 0.46.0

</details>
