## Step 1: Define the symbolic representation of the variables
The variables are 'blue highlighters', '3D printers', 'lanyards', 'hole punches' which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $1.75x_0 + 9.74x_1 + 8.21x_2 + 2.83x_3$.

## Step 3: List all constraints in symbolic notation
1. $26x_0 + 32x_1 + 24x_2 + 7x_3 \leq 445$ (total storage space constraint)
2. $x_0 + 18x_1 + 22x_2 + 22x_3 \leq 445$ (total sustainability score constraint)
3. $x_0 + x_3 \geq 97/26$ (storage space for blue highlighters and hole punches)
4. $32x_1 + 7x_3 \geq 94$ (storage space for 3D printers and hole punches)
5. $24x_2 + 7x_3 \geq 45$ (storage space for lanyards and hole punches)
6. $26x_0 + 24x_2 \geq 49$ (storage space for blue highlighters and lanyards)
7. $x_0 + 22x_3 \geq 64$ (sustainability score for blue highlighters and hole punches)
8. $x_0 + 22x_2 \geq 37$ (sustainability score for blue highlighters and lanyards)
9. $x_0 + 18x_1 + 22x_2 \geq 87$ (sustainability score for blue highlighters, 3D printers, and lanyards)
10. $18x_1 + 22x_2 + 22x_3 \geq 87$ (sustainability score for 3D printers, lanyards, and hole punches)
11. $x_0 + 18x_1 + 22x_2 \geq 55$ (sustainability score for blue highlighters, 3D printers, and lanyards)
12. $18x_1 + 22x_2 + 22x_3 \geq 55$ (sustainability score for 3D printers, lanyards, and hole punches)
13. $-9x_0 + 10x_1 \geq 0$ (relationship between blue highlighters and 3D printers)
14. $26x_0 + 32x_1 \leq 336$ (storage space for blue highlighters and 3D printers)
15. $24x_2 + 7x_3 \leq 433$ (storage space for lanyards and hole punches)
16. $26x_0 + 24x_2 + 7x_3 \leq 398$ (storage space for blue highlighters, lanyards, and hole punches)
17. $26x_0 + 32x_1 + 7x_3 \leq 350$ (storage space for blue highlighters, 3D printers, and hole punches)
18. $32x_1 + 24x_2 + 7x_3 \leq 279$ (storage space for 3D printers, lanyards, and hole punches)
19. $26x_0 + 32x_1 + 24x_2 + 7x_3 \leq 279$ (total storage space for all items)
20. $22x_2 + 22x_3 \leq 291$ (sustainability score for lanyards and hole punches)
21. $x_0 + 22x_3 \leq 414$ (sustainability score for blue highlighters and hole punches)
22. $x_0 + 18x_1 \leq 291$ (sustainability score for blue highlighters and 3D printers)
23. $x_0 + 22x_2 \leq 277$ (sustainability score for blue highlighters and lanyards)
24. $x_0 + 18x_1 + 22x_2 \leq 147$ (sustainability score for blue highlighters, 3D printers, and lanyards)
25. $18x_1 + 22x_2 + 22x_3 \leq 282$ (sustainability score for 3D printers, lanyards, and hole punches)
26. $x_0 + 18x_1 + 22x_2 + 22x_3 \leq 282$ (sustainability score for all items)

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'blue highlighters'),
        ('x1', '3D printers'),
        ('x2', 'lanyards'),
        ('x3', 'hole punches')
    ],
    'objective_function': '1.75*x0 + 9.74*x1 + 8.21*x2 + 2.83*x3',
    'constraints': [
        '26*x0 + 32*x1 + 24*x2 + 7*x3 <= 445',
        'x0 + 18*x1 + 22*x2 + 22*x3 <= 445',
        'x0 + x3 >= 97/26',
        '32*x1 + 7*x3 >= 94',
        '24*x2 + 7*x3 >= 45',
        '26*x0 + 24*x2 >= 49',
        'x0 + 22*x3 >= 64',
        'x0 + 22*x2 >= 37',
        'x0 + 18*x1 + 22*x2 >= 87',
        '18*x1 + 22*x2 + 22*x3 >= 87',
        'x0 + 18*x1 + 22*x2 >= 55',
        '18*x1 + 22*x2 + 22*x3 >= 55',
        '-9*x0 + 10*x1 >= 0',
        '26*x0 + 32*x1 <= 336',
        '24*x2 + 7*x3 <= 433',
        '26*x0 + 24*x2 + 7*x3 <= 398',
        '26*x0 + 32*x1 + 7*x3 <= 350',
        '32*x1 + 24*x2 + 7*x3 <= 279',
        '26*x0 + 32*x1 + 24*x2 + 7*x3 <= 279',
        '22*x2 + 22*x3 <= 291',
        'x0 + 22*x3 <= 414',
        'x0 + 18*x1 <= 291',
        'x0 + 22*x2 <= 277',
        'x0 + 18*x1 + 22*x2 <= 147',
        '18*x1 + 22*x2 + 22*x3 <= 282',
        'x0 + 18*x1 + 22*x2 + 22*x3 <= 282'
    ]
}
```

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

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name='blue highlighters', vtype=gurobi.GRB.INTEGER)
x1 = model.addVar(name='3D printers', vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name='lanyards', vtype=gurobi.GRB.INTEGER)
x3 = model.addVar(name='hole punches', vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(1.75*x0 + 9.74*x1 + 8.21*x2 + 2.83*x3, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(26*x0 + 32*x1 + 24*x2 + 7*x3 <= 445)
model.addConstr(x0 + 18*x1 + 22*x2 + 22*x3 <= 445)
model.addConstr(x0 + x3 >= 97/26)
model.addConstr(32*x1 + 7*x3 >= 94)
model.addConstr(24*x2 + 7*x3 >= 45)
model.addConstr(26*x0 + 24*x2 >= 49)
model.addConstr(x0 + 22*x3 >= 64)
model.addConstr(x0 + 22*x2 >= 37)
model.addConstr(x0 + 18*x1 + 22*x2 >= 87)
model.addConstr(18*x1 + 22*x2 + 22*x3 >= 87)
model.addConstr(x0 + 18*x1 + 22*x2 >= 55)
model.addConstr(18*x1 + 22*x2 + 22*x3 >= 55)
model.addConstr(-9*x0 + 10*x1 >= 0)
model.addConstr(26*x0 + 32*x1 <= 336)
model.addConstr(24*x2 + 7*x3 <= 433)
model.addConstr(26*x0 + 24*x2 + 7*x3 <= 398)
model.addConstr(26*x0 + 32*x1 + 7*x3 <= 350)
model.addConstr(32*x1 + 24*x2 + 7*x3 <= 279)
model.addConstr(26*x0 + 32*x1 + 24*x2 + 7*x3 <= 279)
model.addConstr(22*x2 + 22*x3 <= 291)
model.addConstr(x0 + 22*x3 <= 414)
model.addConstr(x0 + 18*x1 <= 291)
model.addConstr(x0 + 22*x2 <= 277)
model.addConstr(x0 + 18*x1 + 22*x2 <= 147)
model.addConstr(18*x1 + 22*x2 + 22*x3 <= 282)
model.addConstr(x0 + 18*x1 + 22*x2 + 22*x3 <= 282)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('blue highlighters: ', x0.varValue)
    print('3D printers: ', x1.varValue)
    print('lanyards: ', x2.varValue)
    print('hole punches: ', x3.varValue)
else:
    print('The model is infeasible')
```