Inconsistent behavior when calling join/concat on DataFrames with CategoricalIndex and IntervalIndex
#### Code Sample

```python
import pandas as pd
df1 = pd.DataFrame(data={'col1': [1, 1]}, index=pd.interval_range(0, 1, 2))
df2 = pd.DataFrame(data={'col2': [1, 1]}, index=pd.interval_range(0, 1, 2))
df2.index = pd.CategoricalIndex(df2.index)

df2.join(df1) # works
df2.join([df1]) # works
df1.join(df2) # works
df1.join([df2]) # `TypeError: object of type 'NoneType' has no len()`
pd.concat([df1, df2], axis=1) # `TypeError: the other index needs to be an IntervalIndex too, but was type CategoricalIndex`
```

#### Problem description
When joining dataframes with `CategoricalIndex` and `IntervalIndex`, sometimes it does not work. In particular, when the `df` on which we call `join` has `IntervalIndex`, and the argument is a `df` with `CategoricalIndex` and is wrapped in a list, somewhere in `concat.py` throws a `TypeError` with an undecipherable error message. 

`pd.concat` on these DataFrames throws a slightly more helpful message. 

#### Expected Output
I'm not exactly sure what the current rule is regarding how to handle these two index types, but we should make it consistent. I think if the index'es can be matched (possibly after conversion), then the join/concat operation should be allowed. Or maybe give a warning saying some conversion is done implicitly. 


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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-138-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.0
pytest: None
pip: 19.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: None
patsy: None
dateutil: 2.7.2
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.4
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
