To solve the given optimization problem using Gurobi, we need to translate the natural language description into a mathematical formulation that can be represented in code. The problem involves maximizing an objective function subject to several constraints.

The variables are:
- `x0`: The number of reconnaissance troops.
- `x1`: The number of military intelligence companies.

The objective function to maximize is: `1.57*x0 + 9.68*x1`.

Constraints:
1. Deployment weight of reconnaissance troops: `12*x0`.
2. Deployment weight of military intelligence companies: `2*x1`.
3. Total deployment weight must be at least 48 metric tons: `12*x0 + 2*x1 >= 48`.
4. Constraint involving both variables: `-8*x0 + 9*x1 >= 0`.
5. Combined deployment weight must not exceed 138 metric tons: `12*x0 + 2*x1 <= 138`.
6. `x0` (reconnaissance troops) must be an integer.
7. `x1` (military intelligence companies) must also be an integer.

Given these constraints and the objective function, we can write the Gurobi code as follows:

```python
from gurobipy import *

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

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

# Set the objective function
model.setObjective(1.57*x0 + 9.68*x1, GRB.MAXIMIZE)

# Add constraints
model.addConstr(12*x0 + 2*x1 >= 48, "total_weight_min")
model.addConstr(-8*x0 + 9*x1 >= 0, "mixed_constraint")
model.addConstr(12*x0 + 2*x1 <= 138, "total_weight_max")

# Optimize the model
model.optimize()

# Print the solution
if model.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Reconnaissance Troops: {x0.x}")
    print(f"Military Intelligence Companies: {x1.x}")
    print(f"Objective Function Value: {model.objVal}")
else:
    print("No optimal solution found. The model is either infeasible or unbounded.")

```