## Step 1: Define the symbolic representation of the variables
The variables are 'engineer platoons', 'light infantry companies', and 'CBRN platoons', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is $7.58x_0 + 5.44x_1 + 5.56x_2$.

## Step 3: List all the constraints in symbolic notation
1. $2x_0 + 11x_1 \geq 21$
2. $11x_1 + 10x_2 \geq 9$
3. $2x_0 + 11x_1 + 10x_2 \geq 18$
4. $2x_0 + 11x_1 + 10x_2 \geq 18$ (same as 3)
5. $13x_1 + 8x_2 \geq 23$
6. $3x_0 + 8x_2 \geq 19$
7. $3x_0 + 13x_1 + 8x_2 \geq 19$
8. $14x_0 + 14x_2 \geq 9$
9. $14x_0 + 14x_1 + 14x_2 \geq 9$
10. $11x_0 + x_1 \geq 26$
11. $11x_0 + 10x_2 \geq 11$
12. $11x_0 + x_1 + 10x_2 \geq 13$
13. $11x_0 + x_1 + 10x_2 \geq 13$ (same as 12)
14. $-2x_0 + 5x_1 \geq 0$
15. $-2x_1 + x_2 \geq 0$
16. $11x_1 + 10x_2 \leq 32$
17. $2x_0 + 11x_1 \leq 45$
18. $13x_1 + 8x_2 \leq 45$
19. $11x_0 + x_1 \leq 43$

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'engineer platoons'), 
        ('x1', 'light infantry companies'), 
        ('x2', 'CBRN platoons')
    ], 
    'objective_function': '7.58x0 + 5.44x1 + 5.56x2', 
    'constraints': [
        '2x0 + 11x1 >= 21', 
        '11x1 + 10x2 >= 9', 
        '2x0 + 11x1 + 10x2 >= 18', 
        '13x1 + 8x2 >= 23', 
        '3x0 + 8x2 >= 19', 
        '3x0 + 13x1 + 8x2 >= 19', 
        '14x0 + 14x2 >= 9', 
        '14x0 + 14x1 + 14x2 >= 9', 
        '11x0 + x1 >= 26', 
        '11x0 + 10x2 >= 11', 
        '11x0 + x1 + 10x2 >= 13', 
        '-2x0 + 5x1 >= 0', 
        '-2x1 + x2 >= 0', 
        '11x1 + 10x2 <= 32', 
        '2x0 + 11x1 <= 45', 
        '13x1 + 8x2 <= 45', 
        '11x0 + x1 <= 43'
    ]
}
```

## Step 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="engineer_platoons", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="light_infantry_companies", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="CBRN_platoons", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(7.58 * x0 + 5.44 * x1 + 5.56 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2 * x0 + 11 * x1 >= 21)
    model.addConstr(11 * x1 + 10 * x2 >= 9)
    model.addConstr(2 * x0 + 11 * x1 + 10 * x2 >= 18)
    model.addConstr(13 * x1 + 8 * x2 >= 23)
    model.addConstr(3 * x0 + 8 * x2 >= 19)
    model.addConstr(3 * x0 + 13 * x1 + 8 * x2 >= 19)
    model.addConstr(14 * x0 + 14 * x2 >= 9)
    model.addConstr(14 * x0 + 14 * x1 + 14 * x2 >= 9)
    model.addConstr(11 * x0 + x1 >= 26)
    model.addConstr(11 * x0 + 10 * x2 >= 11)
    model.addConstr(11 * x0 + x1 + 10 * x2 >= 13)
    model.addConstr(-2 * x0 + 5 * x1 >= 0)
    model.addConstr(-2 * x1 + x2 >= 0)
    model.addConstr(11 * x1 + 10 * x2 <= 32)
    model.addConstr(2 * x0 + 11 * x1 <= 45)
    model.addConstr(13 * x1 + 8 * x2 <= 45)
    model.addConstr(11 * x0 + x1 <= 43)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Engineer platoons: ", x0.varValue)
        print("Light infantry companies: ", x1.varValue)
        print("CBRN platoons: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```