pd.concat with copy=True doesn't copy columns index
#### Code Sample

```python
import pandas as pd
print(pd.__version__)  # '0.25.3'

# simple frame with a name attribute for columns
df = pd.DataFrame({'a': [1.1], 'b': [2.2]})
df.columns.name = 'x'
print(df)
# x    a    b
# 0  1.1  2.2

# tile rows to a new frame, apparently using the copy of the first frame
df2 = pd.concat([df] * 2, copy=True)
print(df2)
# x    a    b
# 0  1.1  2.2
# 0  1.1  2.2

# clear the column name for the first frame
df.columns.name = None

# and observe the name for the second
assert df2.columns.name is None
print(df2)
#      a    b
# 0  1.1  2.2
# 0  1.1  2.2

# aha, it's the same object
assert df.columns is df2.columns
```

#### Problem description

It is expected that `copy=True` should make a copy of the `columns` index. However, the demonstration shows it's the same object. Modifications, such as the `name` property, are made to the source frame and the output frame created by `pd.concat`.

#### Expected Output

It is expected that `df2.columns` is a copy of the original `df.columns`. This would prevent (e.g.) changing the `name` property for both frames.

A workaround is to manually copy this property after `pd.concat`:
```python
df2 = pd.concat([df] * 2, copy=True)
df2.columns = df2.columns.copy()
```

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

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.3
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 42.0.1.post20191125
Cython           : 0.29.14
pytest           : 5.3.0
hypothesis       : None
sphinx           : 2.2.1
blosc            : None
feather          : None
xlsxwriter       : 1.2.6
lxml.etree       : 4.4.1
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      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.1
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.11
tables           : 3.6.1
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.6
</details>