5G New Radio (NR) introduces several key features to support the new emerging vertical industry use-cases, mainly: (1) Different numerology that gives more flexibility in managing time slot duration, and hence satisfying different delay requirements; (2) Bandwidth part that permits dedicating parts of the bandwidth to ensure different data rate requirements. However, although 5G NR introduces several enhancements, it makes radio resource management, more precisely resource scheduling, more complex and challenging. In this paper, we address the challenge of radio resource management in 5G NR featuring network slicing. We introduce a novel scheduling solution based on Deep Reinforcement Learning (DRL) to allocate resources and numerology for UEs to satisfy their different requirements. We evaluated the solution for different network configurations and compared its performance with the maximum achievable throughput. Simulation results demonstrated the efficiency of the proposed algorithm to allocate resources and the ability to scale for larger bandwidths covering both Frequency Range 1 (FR1) and FR2, as well as serving a higher number of User Equipment (UE).