To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using algebraic notation.

Let's define:
- $x_1$ as the number of signal platoons,
- $x_2$ as the number of artillery batteries.

The objective function is to maximize $9x_1 + 5x_2$.

The constraints can be translated as follows:
1. Deployment weight constraint for signal platoons: $13.07x_1$
2. Logistical capacity constraint for signal platoons: $18.2x_1$
3. Deployment weight constraint for artillery batteries: $18.87x_2$
4. Logistical capacity constraint for artillery batteries: $6.47x_2$
5. Minimum deployment weight for both: $13.07x_1 + 18.87x_2 \geq 18$ (However, given the weights of individual units are already greater than this minimum, this constraint may not affect the solution directly.)
6. Total combined logistical capacity: $18.2x_1 + 6.47x_2 \geq 112$
7. Linear constraint: $6x_1 - 4x_2 \geq 0$
8. Combined deployment weight constraint: $13.07x_1 + 18.87x_2 \leq 31$ (This will likely be the most limiting constraint given the individual weights.)
9. Upper bound for combined logistical capacity: $18.2x_1 + 6.47x_2 \leq 192$
10. Non-negativity and integer constraints: $x_1, x_2 \geq 0$ and $x_1, x_2 \in \mathbb{Z}$

Given these translations, the symbolic representation of the problem is:

```json
{
  'sym_variables': [('x1', 'signal platoons'), ('x2', 'artillery batteries')],
  'objective_function': '9*x1 + 5*x2',
  'constraints': [
    '13.07*x1 + 18.87*x2 <= 31', 
    '18.2*x1 + 6.47*x2 >= 112', 
    '18.2*x1 + 6.47*x2 <= 192', 
    '6*x1 - 4*x2 >= 0',
    'x1 >= 0', 
    'x2 >= 0',
    'x1 == int(x1)',
    'x2 == int(x2)'
  ]
}
```

Now, let's implement this problem in Gurobi Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="signal_platoons")
x2 = m.addVar(vtype=GRB.INTEGER, name="artillery_batteries")

# Set objective function
m.setObjective(9*x1 + 5*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(13.07*x1 + 18.87*x2 <= 31, "deployment_weight")
m.addConstr(18.2*x1 + 6.47*x2 >= 112, "logistical_capacity_min")
m.addConstr(18.2*x1 + 6.47*x2 <= 192, "logistical_capacity_max")
m.addConstr(6*x1 - 4*x2 >= 0, "linear_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Signal Platoons: {x1.x}")
    print(f"Artillery Batteries: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```