## Step 1: Define the symbolic representation of the variables
The variables are 'CBRN platoons', 'water purification units', and 'light infantry companies'. Let's denote them as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $5.01x_1 + 7.22x_2 + 3.34x_3$.

## Step 3: Define the constraints in symbolic notation
1. The mobility rating of CBRN platoons is $1.53x_1$.
2. The mobility rating of water purification units is $5.73x_2$.
3. The mobility rating of light infantry companies is $8.14x_3$.
4. The total combined mobility rating from CBRN platoons and light infantry companies must be 26 at a minimum: $1.53x_1 + 8.14x_3 \geq 26$.
5. The total combined mobility rating from water purification units and light infantry companies must be 24 or more: $5.73x_2 + 8.14x_3 \geq 24$.
6. The total combined mobility rating from CBRN platoons, water purification units, and light infantry companies has to be as much or more than 24: $1.53x_1 + 5.73x_2 + 8.14x_3 \geq 24$.
7. $-1$ times the number of CBRN platoons, plus $6$ times the number of water purification units should be at least zero: $-x_1 + 6x_2 \geq 0$.
8. The total combined mobility rating from CBRN platoons and water purification units should be as much or less than 59: $1.53x_1 + 5.73x_2 \leq 59$.
9. The total combined mobility rating from CBRN platoons, water purification units, and light infantry companies must be at maximum 47: $1.53x_1 + 5.73x_2 + 8.14x_3 \leq 47$.
10. $x_1$, $x_2$, and $x_3$ must be integers.

## 4: Create the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'CBRN platoons'), ('x2', 'water purification units'), ('x3', 'light infantry companies')],
    'objective_function': '5.01x1 + 7.22x2 + 3.34x3',
    'constraints': [
        '1.53x1 + 8.14x3 >= 26',
        '5.73x2 + 8.14x3 >= 24',
        '1.53x1 + 5.73x2 + 8.14x3 >= 24',
        '-x1 + 6x2 >= 0',
        '1.53x1 + 5.73x2 <= 59',
        '1.53x1 + 5.73x2 + 8.14x3 <= 47',
        'x1, x2, x3 are integers'
    ]
}
```

## 5: Implement the problem in Gurobi
```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='water_purification_units', vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name='light_infantry_companies', vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(5.01 * x1 + 7.22 * x2 + 3.34 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(1.53 * x1 + 8.14 * x3 >= 26)
    model.addConstr(5.73 * x2 + 8.14 * x3 >= 24)
    model.addConstr(1.53 * x1 + 5.73 * x2 + 8.14 * x3 >= 24)
    model.addConstr(-x1 + 6 * x2 >= 0)
    model.addConstr(1.53 * x1 + 5.73 * x2 <= 59)
    model.addConstr(1.53 * x1 + 5.73 * x2 + 8.14 * x3 <= 47)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('CBRN platoons: ', x1.varValue)
        print('Water purification units: ', x2.varValue)
        print('Light infantry companies: ', x3.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```