Passing `level` to `reindex` on a multi-index doesn't reindex the desired level
#### Code Sample

```python
import numpy as np
import pandas as pd

category_idx = pd.Index(['A', 'B'])
date_idx = pd.date_range('2018-01', '2018-02', freq='MS')
idx = pd.MultiIndex.from_product([category_idx, date_idx], names=['category', 'date'])

series = pd.Series(np.random.randn(len(category_idx) * len(date_idx)), index=idx)
series
# category  date      
# A         2018-01-01   -0.060216
#           2018-02-01    0.757948
# B         2018-01-01    0.411784
#           2018-02-01   -0.062216
# dtype: float64

new_date_idx = date_idx.union([date_idx[-1] + date_idx.freq])
new_date_idx
# DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01'], dtype='datetime64[ns]', freq='MS')

series.reindex(index=new_date_idx, level='date')
# Where are the NaNs?
# category  date      
# A         2018-01-01   -0.060216
#           2018-02-01    0.757948
# B         2018-01-01    0.411784
#           2018-02-01   -0.062216
# dtype: float64

series.loc['A'].reindex(index=new_date_idx)
# 2018-01-01   -0.060216
# 2018-02-01    0.757948
# 2018-03-01         NaN
# Freq: MS, dtype: float64
```
#### Problem description

Passing `level` to `reindex` on a multi-index doesn't reindex the desired level.

#### Expected Output

In the above example, I'd expect to see two new rows, each containing `NaN`. The expected output is analogous to calling `reindex` on a single-level index.

I originally posted this issue as a question to StackOverflow, where a user kindly posted a workaround: https://stackoverflow.com/questions/54904974/extending-a-datetime-index-within-a-multi-index.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.4.3
Cython: None
numpy: 1.15.3
scipy: 1.2.0
pyarrow: 0.12.1
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.6
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.12
pymysql: 0.9.2
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>