Add realtime_trace_jsonl recipe for structured real-time optimization progress streaming

[MySweetEden]

Motivation

PySCIPOpt already has recipe(s) that store optimization progress in memory. However, in-memory traces are not suitable for real-time, external observation (e.g., another process tailing progress, dashboards, log collectors).

This recipe focuses on the missing piece: a stream-friendly, structured output that can be consumed outside the running Python process during solve.

Design Decisions

• JSONL format: Designed for streaming writes and partial reads; remains readable even if the run is interrupted or crashes
• Real-time external output is the primary value:
  • Records progress updates as one JSON object per line
  • Flushes on key events so downstream consumers can react immediately
• Schema compatibility with setTracefile() (PR Add setTracefile() method for structured optimization progress loggingAdd settracefile api #1158):
  • Uses the same JSONL field names (type, time, primalbound, dualbound, gap, nodes, nsol) for consistency across tracing approaches.
• In-memory + file output: Keeps model.data["trace"] for convenience/testing, but the recipe is centered on file streaming via path=...
• Robust termination signaling for external monitoring:
  • Always emits a final run_end record on normal termination, interruption, or exception
  • On exceptions, run_end includes structured error metadata (status, exception type, message)
  • Flushes run_end to make completion detection reliable

Events Recorded

• bestsol_found: when a new best solution is found
• dualbound_improved: when the dual bound improves
• run_end: when optimization terminates (also emitted on interrupt/exception)

Fields

type, time, primalbound, dualbound, gap, nodes, nsol (aligned with the JSONL trace schema introduced in PR #1158)
(run_end may additionally include: status, exception, message on failure)