feat: add support for sparksql magic

[fangyh20]

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[gemini-code-assist]

Summary of Changes

Hello @fangyh20, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the interactive data analysis experience for users working with Dataproc Spark Connect by integrating Spark SQL magic commands into Jupyter notebooks. This allows users to write and execute SQL queries directly within their notebook cells, streamlining workflows and improving usability for data manipulation and exploration. The changes also ensure broader compatibility with existing Spark ecosystem tools.

Highlights

• Spark SQL Magic Commands Integration: Added support for sparksql-magic library, enabling direct execution of Spark SQL queries within Jupyter notebooks for Dataproc Spark Connect sessions.
• Documentation Update: Updated the README.md with a new section detailing how to use Spark SQL magic commands, including loading the extension, configuring settings, executing queries, and advanced options.
• Session Compatibility Enhancement: Implemented a change to register the DataprocSparkSession instance as pyspark.sql.SparkSession._instantiatedSession to ensure compatibility with tools and libraries that expect this global SparkSession instance.
• Dependency Management: Included IPython and sparksql-magic as required dependencies in setup.py and requirements-dev.txt.
• Integration Tests: Introduced new integration tests to verify the successful loading and functionality of the sparksql-magic library with Dataproc Spark sessions.

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[gemini-code-assist]

Code Review

This pull request adds support for sparksql-magic, allowing users to execute Spark SQL queries in Jupyter notebooks. This is achieved by adding the necessary dependencies and a mechanism to register the DataprocSparkSession with pyspark's global session management. The changes include documentation updates in README.md and new integration tests.

My main feedback is on the implementation in google/cloud/dataproc_spark_connect/session.py, where a local import shadows a top-level import. This can cause confusion and should be addressed to improve code clarity.