[FLINK-39059][models] Add unified inference metrics for model functions

[dubin555]

What is the purpose of the change

The flink-models module (both flink-model-triton and flink-model-openai) currently has no metric instrumentation. Users running model inference in production have no visibility into request rates, error rates, or latency — making it impossible to set up monitoring or alerting for inference degradation.

This PR adds unified inference metrics to both Triton and OpenAI model function base classes, following the same MetricGroup patterns used elsewhere in Flink (e.g., CachingAsyncLookupFunction, AsyncMLPredictRunner).

Four metrics are registered under the model_inference group:

Metric	Type	Description
inference_requests	Counter	Total inference requests initiated
inference_requests_success	Counter	Successful inference completions
inference_requests_failure	Counter	Failed requests (network, HTTP, parse errors)
inference_latency_ms	Gauge	Last inference round-trip time in ms

Brief change log

• Added metric fields and registerMetrics() call in AbstractTritonModelFunction.open() so all Triton subclasses automatically get metrics
• Instrumented all success/failure paths in TritonInferenceModelFunction.asyncPredict() with counter increments and latency tracking
• Added metric fields, registration, and whenComplete() instrumentation in AbstractOpenAIModelFunction.open() / asyncPredict()
• Null inputs and context-overflow-skipped inputs in OpenAI are filtered before incrementing request counts to avoid inflation
• Used a volatile long gauge for latency rather than histogram, since DescriptiveStatisticsHistogram lives in flink-runtime which is not available as a dependency in flink-models

Verifying this change

This change added tests and can be verified as follows:

• Added TritonInferenceMetricsTest — integration test using MockWebServer that verifies metrics are correctly registered and updated after successful inference calls
• Added OpenAIInferenceMetricsTest — integration test using MockWebServer that verifies chat inference metrics and null-input skip behavior
• Existing tests pass unchanged (no behavioral changes to inference logic)

Does this pull request potentially affect one of the following parts:

• Dependencies (does it add or upgrade a dependency): no
• The public API, i.e., is any changed class annotated with @Public(Evolving): no
• The serializers: no
• The runtime per-record code paths (performance sensitive): no
• Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
• The S3 file system connector: no

Documentation

• Does this pull request introduce a new feature? yes
• If yes, how is the feature documented? JavaDocs

[flinkbot]

CI report:

• 2fa6e52 Azure: FAILURE

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