fix(embed): respect physical batch limits for non-causal embeddings

[original4422]

Summary

Fixes #1762. A 513-token BGE-M3 input with n_batch=513 currently reaches a native assertion because the omitted n_ubatch stays at 512.

omitted n_ubatch: embedding -> n_batch; generation -> 512
explicit n_ubatch: retain the requested limit
non-causal/encoder embedding: pack within the native physical batch
single sequence exceeding an explicit physical limit: raise ValueError

Resolve defaults before native context creation, including n_ctx=0 and logical batch clamping. Keep logical truncation unchanged, preserve causal microbatch splitting, and cache the fixed long-context model for the native CI regressions. No vendor changes.

Evidence

• Before: original 0.3.36 / vendor 0c1e57098, real BGE-M3 F16, actual non-causal context: one 513-token sequence and two sequences totaling 256+257 tokens both terminate isolated processes with SIGABRT (encoder requires n_ubatch >= n_tokens). The initial regression set has 5 failures and 3 passing controls.
• After: complete python -m pytest --full-trace -v: 41 passed. This includes real long embeddings, aggregate batches, explicit limits/zero, n_ctx=0, clamping, causal generation/embedding controls, and the existing model tests. Long embeddings retain every token and have finite output values.
• python -m ruff check llama_cpp tests and python -m ruff format --check llama_cpp tests: pass; git diff --check: pass.

Local environment: macOS arm64, Python 3.12.14, original native build with Metal available; the new regression cases explicitly use CPU model layers (n_gpu_layers=0). BGE-M3 F16 revision: 9379ce497e8814b200f2dc0d18eb4045426dcb8c.

Merge Danger

Door: two-way

Blast Radius: embeddings

Omitted embedding microbatches now follow the logical batch size. Explicit physical limits remain supported; a sequence that cannot fit raises a Python error instead of aborting the process. Ordinary generation retains its 512-token default microbatch.