## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'cans of coffee', 'planners', and 'headsets', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $7x_0 + 9x_1 + 1x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $23x_0 + 19x_1 + 24x_2 \leq 480$ (storage space constraint)
2. $8x_0 + 15x_1 + 20x_2 \leq 392$ (sustainability score constraint)
3. $19x_1 + 24x_2 \geq 129$ (storage space with planners and headsets)
4. $23x_0 + 19x_1 \geq 138$ (storage space with cans of coffee and planners)
5. $23x_0 + 19x_1 + 24x_2 \geq 138$ (total storage space)
6. $8x_0 + 20x_2 \geq 108$ (sustainability score from cans of coffee and headsets)
7. $8x_0 + 15x_1 + 20x_2 \geq 108$ (total sustainability score)
8. $9x_1 - 6x_2 \geq 0$ (planners and headsets constraint)
9. $19x_1 + 24x_2 \leq 419$ (maximum storage for planners and headsets)
10. $8x_0 + 15x_1 + 20x_2 \leq 218$ (maximum sustainability score)

## 4: Define the symbolic representation of the problem in JSON format
```json
{
    'sym_variables': [('x0', 'cans of coffee'), ('x1', 'planners'), ('x2', 'headsets')],
    'objective_function': '7*x0 + 9*x1 + 1*x2',
    'constraints': [
        '23*x0 + 19*x1 + 24*x2 <= 480',
        '8*x0 + 15*x1 + 20*x2 <= 392',
        '19*x1 + 24*x2 >= 129',
        '23*x0 + 19*x1 >= 138',
        '23*x0 + 19*x1 + 24*x2 >= 138',
        '8*x0 + 20*x2 >= 108',
        '8*x0 + 15*x1 + 20*x2 >= 108',
        '9*x1 - 6*x2 >= 0',
        '19*x1 + 24*x2 <= 419',
        '8*x0 + 15*x1 + 20*x2 <= 218',
        'x0 % 1 == 0',
        'x1 % 1 == 0',
        'x2 % 1 == 0'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="cans_of_coffee", obj=7, vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="planners", obj=9, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="headsets", obj=1, vtype=gurobi.GRB.INTEGER)

    # Define constraints
    model.addConstr(23*x0 + 19*x1 + 24*x2 <= 480, name="storage_space")
    model.addConstr(8*x0 + 15*x1 + 20*x2 <= 392, name="sustainability_score")
    model.addConstr(19*x1 + 24*x2 >= 129, name="planners_headsets_storage")
    model.addConstr(23*x0 + 19*x1 >= 138, name="coffee_planners_storage")
    model.addConstr(23*x0 + 19*x1 + 24*x2 >= 138, name="total_storage")
    model.addConstr(8*x0 + 20*x2 >= 108, name="coffee_headsets_sustainability")
    model.addConstr(8*x0 + 15*x1 + 20*x2 >= 108, name="total_sustainability")
    model.addConstr(9*x1 - 6*x2 >= 0, name="planners_headsets_constraint")
    model.addConstr(19*x1 + 24*x2 <= 419, name="max_planners_headsets_storage")
    model.addConstr(8*x0 + 15*x1 + 20*x2 <= 218, name="max_sustainability")

    # Set objective function
    model.setObjective(7*x0 + 9*x1 + x2, gurobi.GRB.MINIMIZE)

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Cans of coffee: ", x0.varValue)
        print("Planners: ", x1.varValue)
        print("Headsets: ", x2.varValue)
    else:
        print("Model is infeasible")

solve_optimization_problem()
```