In this paper, we propose a null-space-based preemptive scheduling framework for cross-objective optimization to always guarantee robust URLLC performance, while extracting the maximum possible eMBB capacity. The proposed scheduler perpetually grants incoming URLLC traffic a higher priority for instant scheduling. In case that radio resources are not immediately schedulable, proposed scheduler forcibly enforces an artificial spatial user separation, for the URLLC traffic to get instantly scheduled over shared resources with ongoing eMBB transmissions. A pre-defined reference spatial subspace is constructed for which scheduler instantly picks the active eMBB user whose precoder is the closest possible. Then, it projects the eMBB precoder on-the-go onto the reference subspace, in order for its paired URLLC user to orient its decoder matrix into one possible null space of the reference subspace. Hence, a robust decoding ability is always preserved at the URLLC user, while cross-maximizing the ergodic capacity. Compared to the state-of-the-art proposals from industry and academia, proposed scheduler shows extreme URLLC latency robustness with significantly improved overall spectral efficiency. Analytical analysis and extensive system level simulations are presented to support paper conclusions.