Spark: Support Initial Snapshot Load for Streaming Reads

[alexprosak]

Closes #13188

Implements an initial-snapshot bootstrap for streaming reads of Iceberg tables in Spark. A streaming query started with a fresh checkpoint now reads the current snapshot in full before continuing incrementally - matching how Delta Lake's streaming source behaves by default.

Currently a fresh Iceberg streaming query without options replays the table's history from the oldest ancestor snapshot, which is slow for long-lived tables and any expired/deleted snapshots cannot be streamed. The only existing workarounds (bootstrap via batch read + cut over by timestamp) are fragile, so users can't easily express the common pattern of loading the entire table and then keeping up with new append data.

About the Change

This PR adds a stream-from-snapshot option:

stream-from-snapshot	Behavior
(not set)	new default: read the current snapshot in full, then continue with new snapshots
latest	read snapshots committed after latest on stream startup
earliest	read from the oldest ancestor (the existing default)
<snapshot-id>	read snapshots after the given snapshot id (exclusive)

stream-from-snapshot and stream-from-timestamp are mutually exclusive. Previous default behavior to read from oldest ancestor can be opted-in via stream-from-snapshot=earliest

Follow-ups:

• Docs update with new default behavior & stream-from-snapshot option
• Ports to other supported spark versions