This work details a scalable framework to orchestrate a rotary-wing UAV swarm serving as relays to facilitate beyond line-of-sight connectivity for ground users. First, a Multiscale Adaptive Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the average latency to serve user requests, subject to an average UAV power constraint, the optimization problem is cast as a semi-Markov decision process exhibiting a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the long-term delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost; finally, rate adaptation is embedded along the trajectory to leverage air-to-ground channel conditions. A novel hierarchical competitive swarm optimization scheme is developed to devise high-resolution trajectories. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication, enabled by a decentralized command-and-control network with: 1) spread maximization to proactively position UAVs; 2) consensus-driven conflict resolution to orchestrate scheduling decisions; 3) adaptive frequency reuse to improve spectrum utilization; and 4) piggybacking to allow UAVs to serve multiple ground users simultaneously. Numerical evaluations show that, for user requests of 10 Mbits, generated with a Poisson arrival process with rate 0.2 req/min/UAV, single-agent MAESTRO offers 3.8x faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7x faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.