Poor performance of constructing of DataFrame from series which indexes have different time zones
#### Code Sample, a copy-pastable example if possible

```python
import time

import pandas as pd

a = pd.Series([1, 1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T01:00:00+01:00'),
                                              pd.Timestamp('2019-04-07T01:00:00+01:00')]))
a = a.resample('1s').sum()  # ~500k items

# same TZ
b = pd.Series([1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T02:00:00+01:00')]))
b = b.resample('1s').sum()  # 1 item

# different TZ
c = pd.Series([1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T02:00:00+02:00')]))
c = c.resample('1s').sum()  # 1 item

start = time.time()
pd.DataFrame({'A': a, 'B': b})
print('same TZ:', time.time() - start)

start = time.time()
pd.DataFrame({'A': a, 'C': c})
print('different TZ:', time.time() - start)

```
#### Problem description

DataFrame is constructed from 2 series. The performance degrades significantly when series have indexes with different time zones. The code from above returns:
```
same TZ: 0.037207841873168945
different TZ: 8.304282188415527
```

#### Expected Output

It's expected that performance is the same.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-58-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.16.4
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : None
pytest           : 4.4.1
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.6.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.0.3
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.2.1
sqlalchemy       : None
tables           : 3.5.1
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

</details>
