To solve this optimization problem using Gurobi, we need to translate the given natural language description into a mathematical formulation that can be expressed in code. The problem involves maximizing an objective function subject to several constraints related to fuel demand, logistical capacity, logistics footprint, and other limitations.

The variables are:
- `x0`: Number of reconnaissance troops
- `x1`: Number of pathfinder teams

The objective is to maximize: `5*x0 + 7*x1`

Constraints based on the problem description:
1. Fuel demand constraints:
   - Minimum total fuel demand: `13*x0 + 20*x1 >= 45`
   - Maximum total fuel demand: `13*x0 + 20*x1 <= 58`
2. Logistical capacity constraints:
   - Minimum combined logistical capacity: `13*x0 + 13*x1 >= 45`
   - Maximum combined logistical capacity: `13*x0 + 13*x1 <= 94`
3. Logistics footprint constraints:
   - Minimum total logistics footprint: `19*x0 + 15*x1 >= 41`
   - Maximum total logistics footprint: `19*x0 + 15*x1 <= 77`
4. Additional constraint: `x0 - 7*x1 >= 0`

Since both variables must be non-negative (you can't have a negative number of troops or teams) and integer (non-fractional), we also have:
- `x0 >= 0`, `x1 >= 0`
- `x0` and `x1` are integers.

Now, let's write the Gurobi code to solve this problem:

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

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

# Set the 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 + 20*x1 <= 58, "max_fuel_demand")
m.addConstr(13*x0 + 13*x1 >= 45, "min_logistical_capacity")
m.addConstr(13*x0 + 13*x1 <= 94, "max_logistical_capacity")
m.addConstr(19*x0 + 15*x1 >= 41, "min_logistics_footprint")
m.addConstr(19*x0 + 15*x1 <= 77, "max_logistics_footprint")
m.addConstr(x0 - 7*x1 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Reconnaissance troops: {x0.x}")
    print(f"Pathfinder teams: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```