[PyG] Implement PaiNN (Polarizable Atomistic Interaction Neural Network)

### 🚀 The feature, motivation and pitch PaiNN (Polarizable Atomistic Interaction Neural Network) is a staple for molecular property prediction and MD simulations, offering a superior speed-accuracy trade-off compared to SchNet or DimeNet++. TI is often used as an indsutry standard for benchmarking. While implementations exist in specialist libraries (TorchMD-NET, SchNetPack), a native PyG implementation would allow users to leverage PyG's native batching, torch.compile optimizations, and broader GNN utility suite. Read paper here: https://arxiv.org/pdf/2102.03150 Proposed Implementation: I have a functional implementation and would like to PR it to torch_geometric.nn.models. Key technical details I plan to include: - Equivariant Message Passing: Refactored to inherit from MessagePassing for scalar and vector feature streams. - Performance: Optimized radial basis computations and distance-dependent filters to minimize redundant calculations during inference. - Verification: Incl