## Step 1: Define the symbolic representation of the variables
The variables are 'reconnaissance troops' and 'military intelligence companies', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $1.57x_1 + 9.68x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
1. $12x_1 + 2x_2 \geq 48$
2. $-8x_1 + 9x_2 \geq 0$
3. $12x_1 + 2x_2 \leq 138$
4. $x_1$ and $x_2$ must be integers.

## 4: Notice that constraints 3 and 4 in the problem description are essentially the same
The problem mentions two constraints that are identical: "reconnaissance troops and military intelligence companies must have a combined deployment weight of no more than 138 metric tons." This seems to be a repetition.

## 5: Compile the symbolic representation of the problem
The symbolic representation is:
- Variables: $x_1$ for 'reconnaissance troops', $x_2$ for 'military intelligence companies'
- Objective function: Maximize $1.57x_1 + 9.68x_2$
- Constraints:
  1. $12x_1 + 2x_2 \geq 48$
  2. $-8x_1 + 9x_2 \geq 0$
  3. $12x_1 + 2x_2 \leq 138$
  4. $x_1, x_2 \in \mathbb{Z}$ (integer constraint)

## 6: Convert the problem into Gurobi code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="reconnaissance_troops", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="military_intelligence_companies", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(1.57 * x1 + 9.68 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(12 * x1 + 2 * x2 >= 48, name="deployment_weight_min")
    model.addConstr(-8 * x1 + 9 * x2 >= 0, name="troops_vs_companies")
    model.addConstr(12 * x1 + 2 * x2 <= 138, name="deployment_weight_max")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Reconnaissance troops: {x1.varValue}")
        print(f"Military intelligence companies: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 7: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'reconnaissance troops'), ('x2', 'military intelligence companies')],
    'objective_function': '1.57*x1 + 9.68*x2',
    'constraints': [
        '12*x1 + 2*x2 >= 48',
        '-8*x1 + 9*x2 >= 0',
        '12*x1 + 2*x2 <= 138',
        'x1, x2 are integers'
    ]
}
```