## Step 1: Define the symbolic representation of the variables
The variables are ['lanyards', 'scissors', 'red highlighters', 'manila envelopes'] which can be represented symbolically as ['x0', 'x1', 'x2', 'x3'].

## Step 2: Create a dictionary mapping symbolic variables to their natural language counterparts
The symbolic variables and their corresponding natural language objects are:
- x0: lanyards
- x1: scissors
- x2: red highlighters
- x3: manila envelopes

## Step 3: Formulate the objective function using symbolic variables
The objective function to minimize is $5.87x_0 + 8.98x_1 + 1.13x_2 + 9.47x_3$.

## 4: List all the constraints in symbolic form
Constraints:
1. $4x_0 + 30x_1 + 27x_2 + 24x_3 \leq 782$ (storage space)
2. $24x_0 + 28x_1 + 10x_2 + 4x_3 \leq 401$ (workplace safety impact)
3. $25x_0 + 10x_1 + 7x_2 + 21x_3 \leq 315$ (dollar cost)
4. $27x_2 + 24x_3 \geq 81$ (red highlighters and manila envelopes storage)
5. $4x_0 + 27x_2 \geq 94$ (lanyards and red highlighters storage)
6. $30x_1 + 24x_3 \geq 141$ (scissors and manila envelopes storage)
7. $4x_0 + 27x_2 + 24x_3 \geq 192$ (lanyards, red highlighters, and manila envelopes storage)
8. $30x_1 + 27x_2 + 24x_3 \geq 192$ (scissors, red highlighters, and manila envelopes storage)
9. $4x_0 + 27x_2 + 24x_3 \geq 155$ (lanyards, red highlighters, and manila envelopes storage)
10. $30x_1 + 27x_2 + 24x_3 \geq 155$ (scissors, red highlighters, and manila envelopes storage)
11. $4x_0 + 30x_1 + 27x_2 + 24x_3 \geq 155$ (all items storage)
12. $10x_2 + 4x_3 \geq 53$ (red highlighters and manila envelopes workplace safety)
13. $28x_1 + 10x_2 \geq 40$ (scissors and red highlighters workplace safety)
14. $24x_0 + 10x_2 \geq 37$ (lanyards and red highlighters workplace safety)
15. $24x_0 + 4x_3 \geq 33$ (lanyards and manila envelopes workplace safety)
16. $28x_1 + 4x_3 \geq 87$ (scissors and manila envelopes workplace safety)
17. $24x_0 + 10x_2 + 4x_3 \geq 63$ (lanyards, red highlighters, and manila envelopes workplace safety)
18. $24x_0 + 28x_1 + 10x_2 + 4x_3 \geq 63$ (all items workplace safety)
19. $7x_2 + 21x_3 \geq 27$ (red highlighters and manila envelopes cost)
20. $25x_0 + 21x_3 \geq 53$ (lanyards and manila envelopes cost)
21. $25x_0 + 7x_2 \geq 36$ (lanyards and red highlighters cost)
22. $10x_1 + 7x_2 \geq 39$ (scissors and red highlighters cost)
23. $25x_0 + 10x_1 + 7x_2 + 21x_3 \geq 49$ (all items cost)
24. $6x_1 - 9x_2 \geq 0$ (scissors and red highlighters relation)
25. $-10x_2 + 5x_3 \geq 0$ (red highlighters and manila envelopes relation)
26. $25x_0 + 10x_1 + 21x_3 \leq 109$ (lanyards, scissors, and manila envelopes cost)
27. $25x_0 + 7x_2 + 21x_3 \leq 151$ (lanyards, red highlighters, and manila envelopes cost)

## 5: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'lanyards'),
        ('x1', 'scissors'),
        ('x2', 'red highlighters'),
        ('x3', 'manila envelopes')
    ],
    'objective_function': '5.87*x0 + 8.98*x1 + 1.13*x2 + 9.47*x3',
    'constraints': [
        '4*x0 + 30*x1 + 27*x2 + 24*x3 <= 782',
        '24*x0 + 28*x1 + 10*x2 + 4*x3 <= 401',
        '25*x0 + 10*x1 + 7*x2 + 21*x3 <= 315',
        '27*x2 + 24*x3 >= 81',
        '4*x0 + 27*x2 >= 94',
        '30*x1 + 24*x3 >= 141',
        '4*x0 + 27*x2 + 24*x3 >= 192',
        '30*x1 + 27*x2 + 24*x3 >= 192',
        '4*x0 + 27*x2 + 24*x3 >= 155',
        '30*x1 + 27*x2 + 24*x3 >= 155',
        '4*x0 + 30*x1 + 27*x2 + 24*x3 >= 155',
        '10*x2 + 4*x3 >= 53',
        '28*x1 + 10*x2 >= 40',
        '24*x0 + 10*x2 >= 37',
        '24*x0 + 4*x3 >= 33',
        '28*x1 + 4*x3 >= 87',
        '24*x0 + 10*x2 + 4*x3 >= 63',
        '24*x0 + 28*x1 + 10*x2 + 4*x3 >= 63',
        '7*x2 + 21*x3 >= 27',
        '25*x0 + 21*x3 >= 53',
        '25*x0 + 7*x2 >= 36',
        '10*x1 + 7*x2 >= 39',
        '25*x0 + 10*x1 + 7*x2 + 21*x3 >= 49',
        '6*x1 - 9*x2 >= 0',
        '-10*x2 + 5*x3 >= 0',
        '25*x0 + 10*x1 + 21*x3 <= 109',
        '25*x0 + 7*x2 + 21*x3 <= 151'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(name="x0", vtype=gurobi.GRB.INTEGER)  # lanyards
    x1 = model.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # scissors
    x2 = model.addVar(name="x2", vtype=gurobi.GRB.INTEGER)  # red highlighters
    x3 = model.addVar(name="x3", vtype=gurobi.GRB.INTEGER)  # manila envelopes

    # Objective function
    model.setObjective(5.87 * x0 + 8.98 * x1 + 1.13 * x2 + 9.47 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4 * x0 + 30 * x1 + 27 * x2 + 24 * x3 <= 782)
    model.addConstr(24 * x0 + 28 * x1 + 10 * x2 + 4 * x3 <= 401)
    model.addConstr(25 * x0 + 10 * x1 + 7 * x2 + 21 * x3 <= 315)
    model.addConstr(27 * x2 + 24 * x3 >= 81)
    model.addConstr(4 * x0 + 27 * x2 >= 94)
    model.addConstr(30 * x1 + 24 * x3 >= 141)
    model.addConstr(4 * x0 + 27 * x2 + 24 * x3 >= 192)
    model.addConstr(30 * x1 + 27 * x2 + 24 * x3 >= 192)
    model.addConstr(4 * x0 + 27 * x2 + 24 * x3 >= 155)
    model.addConstr(30 * x1 + 27 * x2 + 24 * x3 >= 155)
    model.addConstr(4 * x0 + 30 * x1 + 27 * x2 + 24 * x3 >= 155)
    model.addConstr(10 * x2 + 4 * x3 >= 53)
    model.addConstr(28 * x1 + 10 * x2 >= 40)
    model.addConstr(24 * x0 + 10 * x2 >= 37)
    model.addConstr(24 * x0 + 4 * x3 >= 33)
    model.addConstr(28 * x1 + 4 * x3 >= 87)
    model.addConstr(24 * x0 + 10 * x2 + 4 * x3 >= 63)
    model.addConstr(24 * x0 + 28 * x1 + 10 * x2 + 4 * x3 >= 63)
    model.addConstr(7 * x2 + 21 * x3 >= 27)
    model.addConstr(25 * x0 + 21 * x3 >= 53)
    model.addConstr(25 * x0 + 7 * x2 >= 36)
    model.addConstr(10 * x1 + 7 * x2 >= 39)
    model.addConstr(25 * x0 + 10 * x1 + 7 * x2 + 21 * x3 >= 49)
    model.addConstr(6 * x1 - 9 * x2 >= 0)
    model.addConstr(-10 * x2 + 5 * x3 >= 0)
    model.addConstr(25 * x0 + 10 * x1 + 21 * x3 <= 109)
    model.addConstr(25 * x0 + 7 * x2 + 21 * x3 <= 151)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x0 (lanyards): ", x0.varValue)
        print("x1 (scissors): ", x1.varValue)
        print("x2 (red highlighters): ", x2.varValue)
        print("x3 (manila envelopes): ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```