[Feature] Apply structured output to an agent's final output only

### Search before asking

- [X] I searched in the [issues](https://github.com/apache/flink-agents/issues) and found nothing similar.

### Description

Follow-up to #280. This issue covers applying structured output to an agent's **final output only**, which #280 scoped as a separate step from the chat-model-layer foundation being added in #843.

**Problem.** When a `ReActAgent` declares an `output_schema`, the schema is honored purely by prompt engineering today: a system prompt instructs the model to emit JSON, and the response text is fence-stripped and parsed afterwards. At the chat-model layer, an intermediate tool-calling turn and the final answering turn are indistinguishable, so there is no point at which structured output can be applied to just the final result. The #843 foundation adds an explicit output-schema parameter to the chat path plus a policy (`auto`/`native`/`prompt`) and connection-side capability split, but no framework path passes a schema on that call yet — supplying it at the right moment is what this issue adds.

**Proposed solution.** Issue a dedicated structured call once the loop has produced its final answer — at `ChatModelAction`'s no-tool-calls branch, the point where the model stops requesting tools. That call passes the output schema explicitly, so native structured output applies to the final output only and intermediate tool-calling turns are untouched.

**Schema instructions must become strategy-driven.** `ReActAgent` currently registers a schema-instruction prompt (`The final response should be json format, and match the schema ...`) unconditionally at construction whenever `output_schema` is set, in both languages:

- Java — `ReActAgent.java:66-87` (registered at `:86`)
- Python — `react_agent.py:128-139`

That prompt is then injected into the messages when the action runs (Java `ReActAgent.java:156-162`, Python `react_agent.py:189-196`).

If the finalization call uses a model that applies structured output natively, the prompt instruction and the provider's own schema enforcement become two overlapping channels describing the same schema. The schema-instruction path therefore needs to follow the selected strategy: emit it for the prompt-based fallback, omit it when the native path applies. Both sites are candidates for carrying that decision — the injection is per-call while the registration happens at construction, before the effective model is known — so which one carries it is part of this issue's design rather than a settled choice.

**Open questions.**

- Should finalization be an extra post-loop model round-trip, or a forced tool call that avoids the additional call? An extra call is the simpler starting point; the forced-tool form is an optimization that could follow.
- Where should `auto` resolve policy into a concrete strategy, given capability is a connection-side predicate over the effective model at request-build time?

**Scope.** Depends on the #843 foundation landing first, since it calls that explicit-schema chat path. Java and Python move together.

### Are you willing to submit a PR?

- [X] I'm willing to submit a PR!