[FLINK-32609] Support Projection Pushdown

[fqshopify]

Implements the SupportsProjectPushdown interface for KafkaDynamicSource

Benefits

1. Improved resiliency
   • Currently SQL queries will fail if any field in the table undergoes a breaking schema change, even if the SQL query itself does not depend on that field.
   • After the changes in this PR, SQL queries will continue to work even if fields that they do not depend on experience breaking schema changes. See testProjectionPushdownWithJsonFormatAndBreakingSchemaChange for an example.
   • This particular improvement is only applicable to Formats that implement the ProjectableDecodingFormat interface and that can decode messages independently/dynamically e.g. json, avro-confluent, debezium-avro-confluent.
2. Improved performance
   • Unneeded columns will be filtered out at an earlier stage of processing (specificially in the TableSourceScan node).
   • The amount of improvement in performance will vary depending on:
     • The number of columns selected
     • The number of columns in the source data
     • The DecodingFormat
     • etc.
   • Note: we cannot push projections all the way down into Kafka.
     • Projection pushdown is typically used as an I/O optimization technique by pushing projections down all the way into the storage layer.
     • Unfortunately the storage layer in our context, Kafka, does not support projection pushdown. As a result, we are not able to push projections down further than the deserialization step of our Flink pipelines.
     • We're still able to improve performance by eliminating unnecessary columns at an earlier stage of processing within our Flink pipeline but the performance benefits are relatively lower than they would have been otherwise.

Limitations

Different formats have different levels of support for projection pushdown:

• Even though the AvroDecodingFormat implements the ProjectableDecodingFormat interface, it appears to have issues with various projections (see FLINK-35324 for more details).
• The CSV format only supports top-level projection pushdown (probably because the CSV format has no standardized way to represent nested fields).
• The JSON format supports both top-level and nested projection pushdown.

To account for the variety of supported projection pushdown levels, we expose a new pair of configs allowing the user to set the level of projection pushdown supported by the key and value format respectively.