Optimize FFN segmentation loading and cleanup for empty and dense subvolumes.

[copybara-service]

Optimize FFN segmentation loading and cleanup for empty and dense subvolumes.

In large-scale sparse pipelines (like whole-brain agglomeration), many subvolumes are empty (contain only background). Running the full connected components and size filtering (clean_up) on these empty arrays is wasteful.

This CL introduces three optimizations with safe corner-case handling:

1. Early exit in load_segmentation: If the loaded segmentation from NPZ is empty, we return early. This avoids a 512MB allocation/cast (astype(np.uint64)) and avoids calling clean_up entirely.
2. Empty Block Fast Path in clean_up / clean_up_and_count: Checks if the array is empty using np.any (takes ~50ms instead of ~1.7s on 400x400x400 empty array). If empty, returns early with properly typed mappings (handling zero-sized arrays as well).
3. Linear-time Size Filtering (clear_dust): For non-empty integer blocks, if the max segment ID is small (< 10M, which is typical for local subvolume IDs before global relabeling), replaces np.unique (which sorts 64M elements) with np.bincount and lookup-table indexing matching data.dtype to avoid memory expansion. This yields up to 17x speedup for dense blocks (from ~3.0s to ~0.17s for 90% density). Properly supports all integer dtypes (including uint8/uint16 without overflow) and signed arrays (including negative segment IDs without positive max), with graceful fallback to np.unique for non-integer arrays.