BUG: apply swallows exceptions, shows inconsistent behaviour
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this bug exists on the master branch of pandas.


### Reproducible Example

```python
import pandas as pd

# The problem case is 2.3

# --- Dataframe with 1 row ---

# 1.1 instantiate dataframe with 1 row.
df_full = pd.DataFrame([{"a": 1, "b": 2, "c": 3}], columns=["a", "b", "c"])
print(f"df_full =\n{df_full}\n")

# 1.2 returns Series with 1 element ("Series([6], dtype: float64)"").
df_full_apply_succeeded = df_full.apply(lambda row: row.sum(), axis=1)
print(
    f"df_full_apply_succeeded =\n{df_full_apply_succeeded}\n"
    f"type = {type(df_full_apply_succeeded)}\n"
)

# 1.3 raises exception correctly.
try:
    df_full_apply_raises = df_full.apply(lambda row: row["not_in_df"], axis=1)
except Exception as e:
    print(f"df_full_apply_raises raises with msg: {e}\n")


# --- Dataframe with no rows ---

# 2.1 instantiate "Empty DataFrame".
df_empty = pd.DataFrame([], columns=["a", "b", "c"])
print(f"df_empty =\n{df_empty}\n")

# 2.2 returns Series with 0 elements ("Series([], dtype: float64)").
# behaviour mirrors example 1.2, consistent for both dataframes.
df_empty_apply_succeeded = df_empty.apply(lambda row: row.sum(), axis=1)
print(
    f"df_empty_apply_succeeded =\n{df_empty_apply_succeeded}\n"
    f"type = {type(df_empty_apply_succeeded)}\n"
)

# 2.3 swallows exception and returns "Empty DataFrame".
# ----> behaviour DIFFERENT from example 1.3, not consistent!
df_empty_apply_does_not_raise = df_empty.apply(lambda row: row["not_in_df"], axis=1)
print(
    f"df_empty_apply_does_not_raise =\n{df_empty_apply_does_not_raise}\n"
    f"type = {type(df_empty_apply_does_not_raise)}\n"
)
```


### Issue Description

__Inconsistency in semantics__

When calling `apply` on a `DataFrame` with >1 elements and the `func` provided to `apply` raises, the exception is propagated to the caller, which is what one would expect.

When calling `apply` on a `DataFrame` with 0 elements and the `func` provided to `apply` raises, the exception is swallowed and `apply` returns an original empty dataframe.

__Inconsistency in return types__

As the code snippet demonstrates, `apply` returns `Series` in all the cases except when it fails on an empty dataframe and swallows the exception - then it returns an empty `DataFrame`. The downstream code eventually breaks but with an error which is hard to track.

This is where exceptions are swallowed: https://github.com/pandas-dev/pandas/blob/22de58e63f9271d4ddb2bf49d008c5a9550c5cc4/pandas/core/apply.py#L779-L782

Because the code doesn't fail right away but returns an unexpected empty `DataFrame` identical to the original, debugging this issue has been hard and time consuming.

### Expected Behavior

It is reasonable to expect that both empty and non-empty dataframes should behave in the same way when it comes to errors raised inside `func` functions provided to `apply` - namely to let them propagate to the user, as it's done in case of non-empty dataframes.

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 73c68257545b5f8530b7044f56647bd2db92e2ba
python           : 3.9.0.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Tue Aug 24 20:28:00 PDT 2021; root:xnu-6153.141.40~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.3
numpy            : 1.21.2
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.0.1
setuptools       : 49.2.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : 7.20.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
