## Step 1: Define the symbolic representation of the variables
The variables are '3D printers', 'paper clips', 'office chairs', and 'lanyards', which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2.05x_0 + 6.16x_1 + 8.22x_2 + 8.27x_3$.

## Step 3: List all the constraints in symbolic notation
1. $16x_0 + 15x_1 + 12x_2 + 2x_3 \leq 192$ (storage space constraint)
2. $x_0 + x_1 + 12x_2 + 17x_3 \leq 129$ (workplace safety impact constraint)
3. $16x_0 + 12x_2 \geq 47$ (3D printers and office chairs storage space)
4. $15x_1 + 12x_2 \geq 19$ (paper clips and office chairs storage space)
5. $16x_0 + 15x_1 + 2x_3 \geq 39$ (3D printers, paper clips, and lanyards storage space)
6. $16x_0 + 15x_1 + 12x_2 + 2x_3 \geq 39$ (all items storage space)
7. $x_0 + 12x_2 \geq 30$ (3D printers and office chairs workplace safety impact)
8. $x_0 + 17x_3 \geq 29$ (3D printers and lanyards workplace safety impact)
9. $x_1 + 17x_3 \geq 16$ (paper clips and lanyards workplace safety impact)
10. $x_0 + x_1 + 12x_2 + 17x_3 \geq 16$ (all items workplace safety impact)
11. $9x_0 - 2x_1 \geq 0$ (3D printers and paper clips constraint)
12. $2x_0 - x_2 \geq 0$ (3D printers and office chairs constraint)
13. $16x_0 + 12x_2 \leq 182$ (3D printers and office chairs storage space limit)
14. $16x_0 + 15x_1 \leq 126$ (3D printers and paper clips storage space limit)
15. $12x_2 + 2x_3 \leq 137$ (office chairs and lanyards storage space limit)
16. $15x_1 + 2x_3 \leq 118$ (paper clips and lanyards storage space limit)
17. $15x_1 + 12x_2 \leq 190$ (paper clips and office chairs storage space limit)
18. $16x_0 + 15x_1 + 2x_3 \leq 140$ (3D printers, paper clips, and lanyards storage space limit)
19. $x_0 + 17x_3 \leq 42$ (3D printers and lanyards workplace safety impact limit)
20. $x_1 + 17x_3 \leq 114$ (paper clips and lanyards workplace safety impact limit)
21. $x_0 + x_1 \leq 112$ (3D printers and paper clips workplace safety impact limit)
22. $x_1 + 12x_2 + 17x_3 \leq 104$ (paper clips, office chairs, and lanyards workplace safety impact limit)

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', '3D printers'), ('x1', 'paper clips'), ('x2', 'office chairs'), ('x3', 'lanyards')],
    'objective_function': '2.05x0 + 6.16x1 + 8.22x2 + 8.27x3',
    'constraints': [
        '16x0 + 15x1 + 12x2 + 2x3 <= 192',
        'x0 + x1 + 12x2 + 17x3 <= 129',
        '16x0 + 12x2 >= 47',
        '15x1 + 12x2 >= 19',
        '16x0 + 15x1 + 2x3 >= 39',
        '16x0 + 15x1 + 12x2 + 2x3 >= 39',
        'x0 + 12x2 >= 30',
        'x0 + 17x3 >= 29',
        'x1 + 17x3 >= 16',
        'x0 + x1 + 12x2 + 17x3 >= 16',
        '9x0 - 2x1 >= 0',
        '2x0 - x2 >= 0',
        '16x0 + 12x2 <= 182',
        '16x0 + 15x1 <= 126',
        '12x2 + 2x3 <= 137',
        '15x1 + 2x3 <= 118',
        '15x1 + 12x2 <= 190',
        '16x0 + 15x1 + 2x3 <= 140',
        'x0 + 17x3 <= 42',
        'x1 + 17x3 <= 114',
        'x0 + x1 <= 112',
        'x1 + 12x2 + 17x3 <= 104'
    ]
}
```

## Step 5: Write the Gurobi code to solve the optimization problem
```python
import gurobipy as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="3D_printers", vtype=gp.GRB.INTEGER)
x1 = m.addVar(name="paper_clips", vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="office_chairs", vtype=gp.GRB.INTEGER)
x3 = m.addVar(name="lanyards", vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(2.05*x0 + 6.16*x1 + 8.22*x2 + 8.27*x3, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(16*x0 + 15*x1 + 12*x2 + 2*x3 <= 192)
m.addConstr(x0 + x1 + 12*x2 + 17*x3 <= 129)
m.addConstr(16*x0 + 12*x2 >= 47)
m.addConstr(15*x1 + 12*x2 >= 19)
m.addConstr(16*x0 + 15*x1 + 2*x3 >= 39)
m.addConstr(16*x0 + 15*x1 + 12*x2 + 2*x3 >= 39)
m.addConstr(x0 + 12*x2 >= 30)
m.addConstr(x0 + 17*x3 >= 29)
m.addConstr(x1 + 17*x3 >= 16)
m.addConstr(x0 + x1 + 12*x2 + 17*x3 >= 16)
m.addConstr(9*x0 - 2*x1 >= 0)
m.addConstr(2*x0 - x2 >= 0)
m.addConstr(16*x0 + 12*x2 <= 182)
m.addConstr(16*x0 + 15*x1 <= 126)
m.addConstr(12*x2 + 2*x3 <= 137)
m.addConstr(15*x1 + 2*x3 <= 118)
m.addConstr(15*x1 + 12*x2 <= 190)
m.addConstr(16*x0 + 15*x1 + 2*x3 <= 140)
m.addConstr(x0 + 17*x3 <= 42)
m.addConstr(x1 + 17*x3 <= 114)
m.addConstr(x0 + x1 <= 112)
m.addConstr(x1 + 12*x2 + 17*x3 <= 104)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("3D printers: ", x0.varValue)
    print("paper clips: ", x1.varValue)
    print("office chairs: ", x2.varValue)
    print("lanyards: ", x3.varValue)
else:
    print("The model is infeasible")
```