SNOW-1818205: Add support for pd.json_normalize

[sfc-gh-helmeleegy]

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

   Fixes SNOW-1818205

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

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

   Add support for pd.json_normalize.

[sfc-gh-nkrishna]

Left one question, but approving

[sfc-gh-rdurrani]

overall lgtm, but I left a couple of comments, and have a few questions:

1. Since we're defaulting to native pandas, should we provide some warning to the user or something, that the data will be processed serially in memory, and may be slow for large datasets?
2. Do we want to consider adding a distributed/threaded approach later on? (e.g. process batches of data records in parallel by loading them into tables (using https://docs.snowflake.com/en/user-guide/tutorials/script-data-load-transform-json), and then joining those tables?)

[sfc-gh-helmeleegy]

overall lgtm, but I left a couple of comments, and have a few questions:

1. Since we're defaulting to native pandas, should we provide some warning to the user or something, that the data will be processed serially in memory, and may be slow for large datasets?
2. Do we want to consider adding a distributed/threaded approach later on? (e.g. process batches of data records in parallel by loading them into tables (using https://docs.snowflake.com/en/user-guide/tutorials/script-data-load-transform-json), and then joining those tables?)

As mentioned in another thread, the input data in this case is already in-memory.