## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'CBRN platoons' and 'airborne infantry companies', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $3x_1 + 6x_2$. The constraints are:
- $17x_1 + 16x_2 \geq 11$
- $17x_1 + 16x_2 \geq 11$ (redundant with the previous one)
- $2x_1 + 17x_2 \geq 12$
- $2x_1 - 8x_2 \geq 0$
- $17x_1 + 16x_2 \leq 36$
- $2x_1 + 17x_2 \leq 27$
- $x_1$ and $x_2$ are integers.

## 2: Correct and simplify the constraints
Correcting and simplifying the constraints:
- The total combined offensive capability rating must be at least 11: $17x_1 + 16x_2 \geq 11$
- The minimum deployment weight is 12 metric tons: $2x_1 + 17x_2 \geq 12$
- The constraint $2x_1 - 8x_2 \geq 0$ can be rewritten as $x_1 \geq 4x_2$
- The total combined offensive capability rating must be at most 36: $17x_1 + 16x_2 \leq 36$
- The total combined deployment weight must be at most 27 metric tons: $2x_1 + 17x_2 \leq 27$

## 3: Define the symbolic representation
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'CBRN platoons'), ('x2', 'airborne infantry companies')],
'objective_function': '3*x1 + 6*x2',
'constraints': [
    '17*x1 + 16*x2 >= 11',
    '2*x1 + 17*x2 >= 12',
    'x1 - 4*x2 >= 0',
    '17*x1 + 16*x2 <= 36',
    '2*x1 + 17*x2 <= 27',
    'x1 >= 0',
    'x2 >= 0',
    'x1 % 1 == 0',
    'x2 % 1 == 0'
]
}
```

## 4: Implement the problem in Gurobi
Now, let's implement this problem in Gurobi using Python.

```python
import gurobi

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

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

    # Define the objective function
    model.setObjective(3*x1 + 6*x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(17*x1 + 16*x2 >= 11, name="offensive_capability_rating_min")
    model.addConstr(2*x1 + 17*x2 >= 12, name="deployment_weight_min")
    model.addConstr(x1 - 4*x2 >= 0, name="CBRN_airborne_ratio")
    model.addConstr(17*x1 + 16*x2 <= 36, name="offensive_capability_rating_max")
    model.addConstr(2*x1 + 17*x2 <= 27, name="deployment_weight_max")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"CBRN platoons: {x1.varValue}")
        print(f"Airborne infantry companies: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```