## Step 1: Define the symbolic representation of the variables
The variables are ['CBRN platoons', 'medical platoons', 'mechanized infantry companies', 'airborne infantry companies']. Let's denote them as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : CBRN platoons
- $x_2$ : medical platoons
- $x_3$ : mechanized infantry companies
- $x_4$ : airborne infantry companies

## 3: Define the objective function in symbolic notation
The objective function to minimize is $8x_1 + 9x_2 + 5x_3 + 5x_4$.

## 4: Define the constraints in symbolic notation
The constraints are:
1. $11x_1 \leq 94$
2. $10x_2 \leq 94$
3. $5x_3 \leq 94$
4. $12x_4 \leq 94$
5. $11x_1 + 5x_3 \geq 17$
6. $10x_2 + 12x_4 \geq 12$
7. $11x_1 + 12x_4 \geq 17$
8. $5x_3 + 12x_4 \geq 16$
9. $10x_2 + 5x_3 \geq 13$
10. $11x_1 + 10x_2 + 5x_3 + 12x_4 \geq 13$
11. $10x_2 - 10x_3 \geq 0$

## 5: Consider the integrality constraints
- $x_1$ must be an integer
- $x_2$ must be an integer
- $x_3$ must be an integer
- $x_4$ must be an integer

## 6: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'CBRN platoons'), ('x2', 'medical platoons'), ('x3', 'mechanized infantry companies'), ('x4', 'airborne infantry companies')],
    'objective_function': '8*x1 + 9*x2 + 5*x3 + 5*x4',
    'constraints': [
        '11*x1 <= 94',
        '10*x2 <= 94',
        '5*x3 <= 94',
        '12*x4 <= 94',
        '11*x1 + 5*x3 >= 17',
        '10*x2 + 12*x4 >= 12',
        '11*x1 + 12*x4 >= 17',
        '5*x3 + 12*x4 >= 16',
        '10*x2 + 5*x3 >= 13',
        '11*x1 + 10*x2 + 5*x3 + 12*x4 >= 13',
        '10*x2 - 10*x3 >= 0'
    ]
}
```

## 7: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="CBRN_platoons", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="medical_platoons", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="mechanized_infantry_companies", vtype=gurobi.GRB.INTEGER)
    x4 = model.addVar(name="airborne_infantry_companies", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(8 * x1 + 9 * x2 + 5 * x3 + 5 * x4, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(11 * x1 <= 94)
    model.addConstr(10 * x2 <= 94)
    model.addConstr(5 * x3 <= 94)
    model.addConstr(12 * x4 <= 94)
    model.addConstr(11 * x1 + 5 * x3 >= 17)
    model.addConstr(10 * x2 + 12 * x4 >= 12)
    model.addConstr(11 * x1 + 12 * x4 >= 17)
    model.addConstr(5 * x3 + 12 * x4 >= 16)
    model.addConstr(10 * x2 + 5 * x3 >= 13)
    model.addConstr(11 * x1 + 10 * x2 + 5 * x3 + 12 * x4 >= 13)
    model.addConstr(10 * x2 - 10 * x3 >= 0)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("CBRN platoons: ", x1.varValue)
        print("Medical platoons: ", x2.varValue)
        print("Mechanized infantry companies: ", x3.varValue)
        print("Airborne infantry companies: ", x4.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```