Fix parallel recovery cap resets and reduce duplicate warnings

[hemanth-asirvatham]

Motivation

• Prevent unintended resets of the worker concurrency cap after error-driven reductions so the system does not jump back to the requested maximum unexpectedly.
• Avoid duplicate output when logging the first rate-limit/connection errors so only a single message is emitted by default.
• Make ramping concurrency back up less conservative by lowering the sustained-success threshold so recovery is faster once stability is observed.

Description

• Updated first-error logging in get_all_responses so _log_rate_limit_once and _log_connection_once only call logger.warning when message_verbose is false, avoiding duplicate prints and warnings.
• Changed _halt_ramp_up to print when message_verbose is enabled and otherwise issue a single logger.warning, removing duplicate messaging.
• Prevented throughput tuning from overwriting a previously reduced concurrency_cap by removing the unintended assignment that reset the cap during dynamic tuning.
• Lowered the sustained-success threshold multiplier used to grow concurrency from 2.5x to 1.5x of the current cap to speed recovery after stable periods.
• All changes are in src/gabriel/utils/openai_utils.py and focus on parallel recovery/logging behavior.

Testing

• No automated tests were run for this change.

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Codex Task

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