## Step 1: Define the symbolic representation of the variables
The variables are ['headsets', 'rubber bands', 'planners', 'hole punches'] which can be represented symbolically as ['x0', 'x1', 'x2', 'x3'].

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- x0: headsets
- x1: rubber bands
- x2: planners
- x3: hole punches

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is: 2*x0 + 3*x1 + 5*x2 + 7*x3.

## Step 4: List the constraints in symbolic notation
The constraints are:
- 0.5*x0 + 2.54*x1 + 0.12*x2 + 2.75*x3 <= 222 (storage space)
- 1.49*x0 + 0.16*x1 + 0.33*x2 + 2.73*x3 <= 231 (weight)
- 0.5*x0 + 0.12*x2 + 2.75*x3 >= 45 (storage space on headsets, planners, and hole punches)
- 0.5*x0 + 2.54*x1 + 0.12*x2 >= 45 (storage space on headsets, rubber bands, and planners)
- 0.5*x0 + 0.12*x2 + 2.75*x3 >= 36 (storage space on headsets, planners, and hole punches)
- 0.5*x0 + 2.54*x1 + 0.12*x2 >= 36 (storage space on headsets, rubber bands, and planners)
- 1.49*x0 + 0.16*x1 >= 39 (weight of headsets and rubber bands)
- 0.16*x1 + 0.33*x2 >= 54 (weight of rubber bands and planners)
- 0.5*x0 + 2.54*x1 <= 164 (storage space for headsets and rubber bands)
- 2.54*x1 + 0.12*x2 <= 104 (storage space for rubber bands and planners)
- 0.5*x0 + 0.12*x2 <= 101 (storage space for headsets and planners)
- 2.54*x1 + 2.75*x3 <= 92 (storage space for rubber bands and hole punches)
- 0.5*x0 + 2.54*x1 + 0.12*x2 + 2.75*x3 <= 92 (total storage space)
- 0.16*x1 + 0.33*x2 <= 177 (weight of rubber bands and planners)
- 1.49*x0 + 0.33*x2 <= 190 (weight of headsets and planners)
- 0.33*x2 + 2.73*x3 <= 85 (weight of planners and hole punches)
- 1.49*x0 + 0.16*x1 + 0.33*x2 + 2.73*x3 <= 85 (total weight)

## 5: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'headsets'), 
        ('x1', 'rubber bands'), 
        ('x2', 'planners'), 
        ('x3', 'hole punches')
    ], 
    'objective_function': '2*x0 + 3*x1 + 5*x2 + 7*x3', 
    'constraints': [
        '0.5*x0 + 2.54*x1 + 0.12*x2 + 2.75*x3 <= 222',
        '1.49*x0 + 0.16*x1 + 0.33*x2 + 2.73*x3 <= 231',
        '0.5*x0 + 0.12*x2 + 2.75*x3 >= 45',
        '0.5*x0 + 2.54*x1 + 0.12*x2 >= 45',
        '0.5*x0 + 0.12*x2 + 2.75*x3 >= 36',
        '0.5*x0 + 2.54*x1 + 0.12*x2 >= 36',
        '1.49*x0 + 0.16*x1 >= 39',
        '0.16*x1 + 0.33*x2 >= 54',
        '0.5*x0 + 2.54*x1 <= 164',
        '2.54*x1 + 0.12*x2 <= 104',
        '0.5*x0 + 0.12*x2 <= 101',
        '2.54*x1 + 2.75*x3 <= 92',
        '0.5*x0 + 2.54*x1 + 0.12*x2 + 2.75*x3 <= 92',
        '0.16*x1 + 0.33*x2 <= 177',
        '1.49*x0 + 0.33*x2 <= 190',
        '0.33*x2 + 2.73*x3 <= 85',
        '1.49*x0 + 0.16*x1 + 0.33*x2 + 2.73*x3 <= 85',
        'x0 >= 0', 'x1 >= 0', 'x2 >= 0', 'x3 >= 0',
        'x0 % 1 == 0', 'x1 % 1 == 0', 'x2 % 1 == 0', 'x3 % 1 == 0'
    ]
}
```

## 6: Write the Gurobi code
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(name="headsets", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="rubber_bands", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="planners", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="hole_punches", vtype=gurobi.GRB.INTEGER)

    # Define objective function
    model.setObjective(2*x0 + 3*x1 + 5*x2 + 7*x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(0.5*x0 + 2.54*x1 + 0.12*x2 + 2.75*x3 <= 222)
    model.addConstr(1.49*x0 + 0.16*x1 + 0.33*x2 + 2.73*x3 <= 231)
    model.addConstr(0.5*x0 + 0.12*x2 + 2.75*x3 >= 45)
    model.addConstr(0.5*x0 + 2.54*x1 + 0.12*x2 >= 45)
    model.addConstr(0.5*x0 + 0.12*x2 + 2.75*x3 >= 36)
    model.addConstr(0.5*x0 + 2.54*x1 + 0.12*x2 >= 36)
    model.addConstr(1.49*x0 + 0.16*x1 >= 39)
    model.addConstr(0.16*x1 + 0.33*x2 >= 54)
    model.addConstr(0.5*x0 + 2.54*x1 <= 164)
    model.addConstr(2.54*x1 + 0.12*x2 <= 104)
    model.addConstr(0.5*x0 + 0.12*x2 <= 101)
    model.addConstr(2.54*x1 + 2.75*x3 <= 92)
    model.addConstr(0.5*x0 + 2.54*x1 + 0.12*x2 + 2.75*x3 <= 92)
    model.addConstr(0.16*x1 + 0.33*x2 <= 177)
    model.addConstr(1.49*x0 + 0.33*x2 <= 190)
    model.addConstr(0.33*x2 + 2.73*x3 <= 85)
    model.addConstr(1.49*x0 + 0.16*x1 + 0.33*x2 + 2.73*x3 <= 85)
    model.addConstr(x0 >= 0)
    model.addConstr(x1 >= 0)
    model.addConstr(x2 >= 0)
    model.addConstr(x3 >= 0)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Headsets: {x0.varValue}")
        print(f"Rubber bands: {x1.varValue}")
        print(f"Planners: {x2.varValue}")
        print(f"Hole punches: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```