The provided resource constraints and objective function are translated into a Gurobi optimization model. The variables `x0` (reconnaissance troops) and `x1` (pathfinder teams) are defined as integers. The objective function maximizes `5*x0 + 7*x1`.  Constraints are added based on fuel demand, logistical capacity, and logistics footprint, incorporating both lower and upper bounds as specified in the problem description. Redundant constraints (e.g., repeating the same upper bound) are included as they were present in the input.

```python
from gurobipy import Model, GRB

# Create a new model
m = Model("resource_allocation")

# Create variables
x0 = m.addVar(vtype=GRB.INTEGER, name="reconnaissance_troops")
x1 = m.addVar(vtype=GRB.INTEGER, name="pathfinder_teams")

# Set objective function
m.setObjective(5 * x0 + 7 * x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(13 * x0 + 20 * x1 >= 45, "min_fuel_demand")
m.addConstr(13 * x0 + 13 * x1 >= 45, "min_logistical_capacity")
m.addConstr(19 * x0 + 15 * x1 >= 41, "min_logistics_footprint")
m.addConstr(1 * x0 - 7 * x1 >= 0, "custom_constraint_1")

m.addConstr(13 * x0 + 20 * x1 <= 58, "max_fuel_demand_1")
m.addConstr(13 * x0 + 20 * x1 <= 58, "max_fuel_demand_2") # Redundant constraint
m.addConstr(13 * x0 + 13 * x1 <= 94, "max_logistical_capacity_1")
m.addConstr(13 * x0 + 13 * x1 <= 94, "max_logistical_capacity_2") # Redundant constraint
m.addConstr(19 * x0 + 15 * x1 <= 77, "max_logistics_footprint_1")
m.addConstr(19 * x0 + 15 * x1 <= 77, "max_logistics_footprint_2") # Redundant constraint


# Resource upper bounds (not explicitly mentioned as constraints in the problem description, but included from the context)
m.addConstr(13 * x0 + 20 * x1 <= 116, "fuel_upper_bound")
m.addConstr(13 * x0 + 13 * x1 <= 106, "logistical_capacity_upper_bound")
m.addConstr(19 * x0 + 15 * x1 <= 120, "logistics_footprint_upper_bound")


# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('x0:', x0.x)
    print('x1:', x1.x)
elif m.status == GRB.INFEASIBLE:
    print('Model is infeasible')
else:
    print('Optimization ended with status %d' % m.status)

```
