Trying to access non-existent df column throws KeyError for wrong key
#### Code Sample, a copy-pastable example if possible

I ran into this on a more complicated `groupby().apply(lambda: ...)` call, but reproduced a simple version below of what I think seems like a bug (or at least undesirable behavior).

```python

import pandas as pd

# simple dataframe
test = pd.DataFrame({'var': ['a', 'a', 'b', 'b'], 'val': range(4)})

# simulated mistake of asking for a 'vau' column, not 'val'
test.groupby('var').apply(lambda rows: pd.DataFrame({'var': [rows.iloc[-1]['var']],
                                                     'val': [rows.iloc[-1]['vau']]}))
```

Doing this yields:

```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_value(self, series, key)
   4735             try:
-> 4736                 return libindex.get_value_box(s, key)
   4737             except IndexError:

pandas/_libs/index.pyx in pandas._libs.index.get_value_box()

pandas/_libs/index.pyx in pandas._libs.index.get_value_at()

pandas/_libs/util.pxd in pandas._libs.util.get_value_at()

pandas/_libs/util.pxd in pandas._libs.util.validate_indexer()

TypeError: 'str' object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:

----------  8<  [snip]  >8 ----------

KeyError: 'var'
```

#### Problem description

When attempting to troubleshoot this, `KeyError: 'foo'` is misleading, as it doesn't point to the key that failed, but one that succeeded. In my actual code, I was doing this for more variables, so while this case is trivial to troubleshoot, real world cases might be more confusing and challenging to figure out for the user.

#### Expected Output

A `KeyError: 'foo'` where `foo` is the incorrect key, not a successful one.

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

<details>

>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.3.1-arch1-1-ARCH
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.1
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.0.3
setuptools       : 41.2.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : 2.6.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

</details>