GridPatchDataset caching can repeat, drop, or reject samples

Describe the bug

GridPatchDataset(cache=True) does not preserve dataset contents for several documented cache configurations:

1. With a partial cache (0 < cache_rate < 1), an uncached item following a cached item reuses the previous item's cache index and emits the cached patches again.
2. With with_coordinates=False, cached patches are zipped with an empty coordinate cache, so no cached patches are emitted.
3. With cache_rate=0 (or cache_num=0), initialization tries to unpack an empty cache and raises ValueError.
4. With a transform pipeline containing no random transform, cached iteration passes start=None to Compose and raises ValueError.

These are data-correctness issues: caching can silently replace or drop training samples, or reject valid cache configurations.

To Reproduce

from monai.data import GridPatchDataset
from monai.transforms import Lambda


def patches(image):
    for item in image:
        yield item, item * 10


partial = GridPatchDataset(
    [[1], [2]], patches, cache=True, cache_rate=0.5, progress=False
)
print(list(partial))
# Actual:   [(1, 10), (1, 10)]
# Expected: [(1, 10), (2, 20)]

no_coordinates = GridPatchDataset(
    [[1, 2]], patches, with_coordinates=False, cache=True, progress=False
)
print(list(no_coordinates))
# Actual:   []
# Expected: [1, 2]

zero_cache = GridPatchDataset(
    [[1], [2]], patches, cache=True, cache_rate=0, progress=False
)
# Actual: ValueError: not enough values to unpack (expected 2, got 0)
# Expected: construct successfully and iterate without using a cache

deterministic = GridPatchDataset(
    [[1]], patches, transform=Lambda(lambda x: x + 100), cache=True, progress=False
)
print(list(deterministic))
# Actual:   ValueError: 'start' (None) cannot be None
# Expected: [(101, 10)]

Expected behavior

Enabling caching must not change which patches are yielded. cache_rate should only select how many source items are cached, and with_coordinates should only control whether coordinates are included in each yielded item. Deterministic transforms should be computed while populating the cache, and a cache hit should resume at the end of that transform pipeline.

Environment

MONAI version: 0+untagged.3487.gd1306f6
MONAI rev id: d1306f6d1996cffeb9d10984dd1c056b7fe2ed1d
Python version: 3.12.0
NumPy version: 2.5.2
PyTorch version: 2.14.0+cpu
OS: Windows

Additional context

The partial-cache issue comes from cache_index being initialized before the image loop and not reset for cache misses. The coordinate-free path always calls zip(data, other) even though _cache_other is intentionally empty when with_coordinates=False. set_data() unconditionally unpacks zip(*self._fill_cache(...)), including when the configured cache size is zero. Finally, Compose.get_index_of_first(...) returns None when every transform is deterministic, but the cache-read path uses that value as the start index.

The cache implementation was introduced in #7180. Existing coverage exercises a full cache with coordinates enabled and a pipeline containing a random transform, so these paths are currently untested.