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feat : support spark compatible int to timestamp cast (#20555)
## Which issue does this PR close? <!-- We generally require a GitHub issue to be filed for all bug fixes and enhancements and this helps us generate change logs for our releases. You can link an issue to this PR using the GitHub syntax. For example `Closes #123` indicates that this PR will close issue #123. --> - Closes #20554 Ref : #20555 ## Rationale for this change <!-- Why are you proposing this change? If this is already explained clearly in the issue then this section is not needed. Explaining clearly why changes are proposed helps reviewers understand your changes and offer better suggestions for fixes. --> ## What changes are included in this PR? 1. Created a new cast scalar UDF to support int to timestamp casts (spark compatible). The goal is to leverage this is as an entry point for all spark compatible / df incompatible casts <!-- There is no need to duplicate the description in the issue here but it is sometimes worth providing a summary of the individual changes in this PR. --> ## Are these changes tested? 1. Yes (through series of unit tests testing various edge cases like overflow , null input etc) <!-- We typically require tests for all PRs in order to: 1. Prevent the code from being accidentally broken by subsequent changes 3. Serve as another way to document the expected behavior of the code If tests are not included in your PR, please explain why (for example, are they covered by existing tests)? --> ## Are there any user-facing changes? 1. Yes. We are bringing in ability to leverage spark compatible cast operations in DF through `spark_cast` as a first step. Next step would be to make changes to the planner to leverage spark compatible cast instead of regular cast operations <!-- If there are user-facing changes then we may require documentation to be updated before approving the PR. --> <!-- If there are any breaking changes to public APIs, please add the `api change` label. -->
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