pd.concat inconsistent with non-unique index
#### Code Sample, a copy-pastable example if possible

Following works:

```py
df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george'], ['a', 'b']],
                   columns=['animal', 'name'])
df1 = pd.DataFrame([['a', 1], ['b', 2]],
                   columns=['letter', 'number'])

df4.index = [0, 1, 1]

pd.concat([df1, df4], axis=1)

	letter	number	animal	name
0	a	1	bird	polly
1	b	2	monkey	george
1	b	2	a	b
```

But if the order of index values is the opposite, it raises ValueError:


```py
df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george'], ['a', 'b']],
                   columns=['animal', 'name'])
df1 = pd.DataFrame([['a', 1], ['b', 2]],
                   columns=['letter', 'number'])

df4.index = [1, 0, 0]

pd.concat([df1, df4], axis=1)

ValueError: Shape of passed values is (3, 4), indices imply (2, 4)
```

#### Problem description

As shown in the example, `concat` behaves inconsistently if index has duplicated value. It depends on the order of values if it succeeds or not. It is causing issues in geopandas (https://github.com/geopandas/geopandas/issues/1223). I know that duplicated index is not ideal thing to have, but this seems to be a bug.

#### Expected Output

Expected output would be concatenated gdf as in the example with sorted index. 

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.0.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.2.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.3
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 42.0.2.post20191201
Cython           : None
pytest           : 5.3.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.10.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.2
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

</details>
