## Step 1: Define the symbolic representation of the variables
The variables are 'artillery batteries', 'armored companies', and 'signal platoons', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : artillery batteries
- $x_1$ : armored companies
- $x_2$ : signal platoons

## 3: Define the objective function in symbolic notation
The objective function to minimize is $7.17x_0 + 2.28x_1 + 2.97x_2$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $2x_0 + 9x_1 + 13x_2 \geq 49$ (total combined deployment weight of at least 49 metric tons)
- $9x_1 + 13x_2 \geq 33$ (total deployment weight of armored companies and signal platoons must be at least 33 metric tons)
- $2x_0 + 9x_1 \geq 53$ (minimum deployment weight for artillery batteries and armored companies is 53 metric tons)
- $7x_0 + 5x_1 \geq 43$ (total combined offensive capability rating from artillery batteries plus armored companies should be as much or more than 43)
- $7x_0 + 5x_1 + 6x_2 \geq 52$ (total combined offensive capability rating from artillery batteries, armored companies, and signal platoons must be as much or more than 52)
- $x_0, x_1, x_2 \geq 0$ and are integers (whole number of artillery batteries, armored companies, and signal platoons)

## 5: Specify the upper bounds for resources
Given resources:
- $r_0$: deployment weight with $x_0 = 2$, $x_1 = 9$, $x_2 = 13$ and an upper bound of 214
- $r_1$: offensive capability rating with $x_0 = 7$, $x_1 = 5$, $x_2 = 6$ and an upper bound of 238

## 6: Formulate the problem in Gurobi
We will use Gurobi to solve this linear integer programming problem.

## 7: Write the Gurobi code
```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="artillery_batteries", vtype=gp.GRB.INTEGER)
x1 = m.addVar(name="armored_companies", vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="signal_platoons", vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(7.17 * x0 + 2.28 * x1 + 2.97 * x2, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(2 * x0 + 9 * x1 + 13 * x2 >= 49, name="total_deployment_weight")
m.addConstr(9 * x1 + 13 * x2 >= 33, name="armored_signal_deployment")
m.addConstr(2 * x0 + 9 * x1 >= 53, name="artillery_armored_deployment")
m.addConstr(7 * x0 + 5 * x1 >= 43, name="artillery_armored_capability")
m.addConstr(7 * x0 + 5 * x1 + 6 * x2 >= 52, name="total_capability")

# Add bounds for resources if necessary
# Not directly needed as we have constraints

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Artillery Batteries: ", x0.varValue)
    print("Armored Companies: ", x1.varValue)
    print("Signal Platoons: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 8: Symbolic Representation
```json
{
    'sym_variables': [('x0', 'artillery batteries'), ('x1', 'armored companies'), ('x2', 'signal platoons')],
    'objective_function': '7.17*x0 + 2.28*x1 + 2.97*x2',
    'constraints': [
        '2*x0 + 9*x1 + 13*x2 >= 49',
        '9*x1 + 13*x2 >= 33',
        '2*x0 + 9*x1 >= 53',
        '7*x0 + 5*x1 >= 43',
        '7*x0 + 5*x1 + 6*x2 >= 52',
        'x0, x1, x2 are integers'
    ]
}
```