The branching geometry of biological transport networks is canonically characterized by a diameter scaling exponent $\alpha$. Traditionally, this exponent interpolates between two structural attractors: impedance matching ($\alpha\approx2$) for pulsatile wave propagation and viscous-metabolic minimization ($\alpha=3$) for steady flow. We demonstrate that neither mechanism in isolation can predict the empirically observed $\alpha_{\mathrm{exp}} = 2.70 \pm 0.20$ in mammalian arterial trees. Incorporating the empirical sub-linear vessel-wall scaling $h(r) \propto r^p$ ($p\approx0.77$) into a three-term metabolic cost function rigorously breaks the universality of Murray's cubic law---a consequence of cost-function non-homogeneity established via Cauchy's functional equation---and bounds the static transport optimum to $\alpha_t \in [2.90, 2.94]$. To account for the dynamic pulsatile environment, we formulate a unified network-level Lagrangian balancing wave-reflection penalties against steady transport-metabolic costs. Because the operational duty cycle $\eta$ between pulsatile and steady states is inherently uncertain over developmental timescales, we cast the morphological optimization as a zero-sum game between network architecture and environmental state. By the minimax theorem---proved here requiring only continuity and strict monotonicity, without global convexity assumptions---the unique saddle point $(\alpha^*, \eta^*)$ satisfies the exact equal-cost condition $\mathcal{C}_{\mathrm{wave}}(\alpha^*) = \mathcal{C}_{\mathrm{transport}}(\alpha^*)$, eliminating $\eta$ as a free parameter. For porcine coronary arteries, this deterministic ground state yields $\alpha^* = 2.72$, while Bayesian marginalization via Monte Carlo over physiological parameter uncertainty predicts a population expectation of $\alpha^*_{\mathrm{MC}} = 2.86 \pm 0.06$. Without requiring fitted parameters, the framework rigorously derives the observed cardiovascular scaling and reduces exactly to Murray's law when dynamic wave modes are absent.