Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload.