Add a functionality in apply_in_pandas to support spark api

[sfc-gh-dyadav]

1. Which Jira issue is this PR addressing? Make sure that there is an accompanying issue to your PR.

   Fixes SNOW-1800723

If the dataframe comes from spark the column names are not same as the spark column names but the function would be assuming the spark column names and operating like that, this change resolves that issue
This adds a functionality of (key, dataframe) which can also be the type of function spark support

2. Fill out the following pre-review checklist:

   • I am adding a new automated test(s) to verify correctness of my new code
     • If this test skips Local Testing mode, I'm requesting review from @snowflakedb/local-testing
   • I am adding new logging messages
   • I am adding a new telemetry message
   • I am adding new credentials
   • I am adding a new dependency
   • If this is a new feature/behavior, I'm adding the Local Testing parity changes.
   • I acknowledge that I have ensured my changes to be thread-safe. Follow the link for more information: Thread-safe Developer Guidelines

The test for this will be added in this PR https://github.com/snowflakedb/sas/pull/725/files, this is a fork introduced for the non public usecase of snowpark library

3. Please describe how your code solves the related issue.

   Please write a short description of how your code change solves the related issue.
   I am extracting the spark names from the column_map which will only be present if this is being sent from the accelerated spark layer.

[sfc-gh-snowflakedb-snyk-sa]

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[sfc-gh-aalam]

Suggested change

	if key_columns is not None:
	import numpy as np
	key_list = [pdf[key].iloc[0] for key in key_columns]
	numpy_array = np.array(key_list)
	keys = tuple(numpy_array)
	if original_columns is not None:
	pdf.columns = original_columns
	if key_columns is not None:
	return func(keys, pdf)
	if original_columns is not None:
	pdf.columns = original_columns
	if key_columns is not None:
	import numpy as np
	key_list = [pdf[key].iloc[0] for key in key_columns]
	numpy_array = np.array(key_list)
	keys = tuple(numpy_array)
	return func(keys, pdf)

nit: can we restructure it this way? grouping the if statements together