chore: null value passthrough for path columns in OpenAIPrompt

[levscaut]

Related Issues/PRs

#xxx

What changes are proposed in this pull request?

Adding a null passthrough mechanism for path columns in OpenAIPrompt. That if all of path columns are null, we passthrough null to underlying service which will also result in null.

How is this patch tested?

• I have written tests (not required for typo or doc fix) and confirmed the proposed feature/bug-fix/change works.

Does this PR change any dependencies?

• No. You can skip this section.
• Yes. Make sure the dependencies are resolved correctly, and list changes here.

Does this PR add a new feature? If so, have you added samples on website?

• No. You can skip this section.
• Yes. Make sure you have added samples following below steps.

1. Find the corresponding markdown file for your new feature in website/docs/documentation folder.
   Make sure you choose the correct class estimators/transformers and namespace.
2. Follow the pattern in markdown file and add another section for your new API, including pyspark, scala (and .NET potentially) samples.
3. Make sure the DocTable points to correct API link.
4. Navigate to website folder, and run yarn run start to make sure the website renders correctly.
5. Don't forget to add <!--pytest-codeblocks:cont--> before each python code blocks to enable auto-tests for python samples.
6. Make sure the WebsiteSamplesTests job pass in the pipeline.

[github-actions]

Hey @levscaut 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

We use semantic commit messages to streamline the release process.
Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix.
This helps us to create release messages and credit you for your hard work!

Examples of commit messages with semantic prefixes:

• fix: Fix LightGBM crashes with empty partitions
• feat: Make HTTP on Spark back-offs configurable
• docs: Update Spark Serving usage
• build: Add codecov support
• perf: improve LightGBM memory usage
• refactor: make python code generation rely on classes
• style: Remove nulls from CNTKModel
• test: Add test coverage for CNTKModel

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[levscaut]

/azp run

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[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 84.43%. Comparing base (4c45b9a) to head (eb9cd27).

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #2463      +/-   ##
==========================================
+ Coverage   81.82%   84.43%   +2.60%     
==========================================
  Files         335      335              
  Lines       17747    17750       +3     
  Branches     1611     1622      +11     
==========================================
+ Hits        14522    14987     +465     
+ Misses       3225     2763     -462     

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[ranadeepsingh]

Please address Brendan's review comments - else lgtm

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[levscaut]

/azp run

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