To tackle this optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

Let's denote:
- $x_1$ as the number of rubber bands,
- $x_2$ as the number of monochrome printers,
- $x_3$ as the number of cans of coffee.

The objective function to minimize is given by: $5x_1 + 7x_2 + 4x_3$.

Now, let's list out the constraints based on the problem description:
1. Usefulness rating constraints:
   - Rubber bands: $19x_1$
   - Monochrome printers: $2x_2$
   - Cans of coffee: $9x_3$
2. Combined usefulness ratings constraints:
   - $19x_1 + 9x_3 \geq 67$
   - $2x_2 + 9x_3 \geq 105$
   - $19x_1 + 2x_2 \geq 61$
   - $19x_1 + 2x_2 + 9x_3 \geq 67$ (noted twice, but we'll consider it once)
3. Additional constraints:
   - $-8x_1 + 5x_3 \geq 0$
   - $-x_2 + 8x_3 \geq 0$
4. Upper bound constraints on combined usefulness ratings:
   - $19x_1 + 9x_3 \leq 311$
   - $19x_1 + 2x_2 \leq 174$

All variables ($x_1$, $x_2$, $x_3$) must be integers.

Thus, the symbolic representation of our problem in JSON format is:
```json
{
    'sym_variables': [('x1', 'rubber bands'), ('x2', 'monochrome printers'), ('x3', 'cans of coffee')],
    'objective_function': '5*x1 + 7*x2 + 4*x3',
    'constraints': [
        '19*x1 + 9*x3 >= 67',
        '2*x2 + 9*x3 >= 105',
        '19*x1 + 2*x2 >= 61',
        '19*x1 + 2*x2 + 9*x3 >= 67',
        '-8*x1 + 5*x3 >= 0',
        '-x2 + 8*x3 >= 0',
        '19*x1 + 9*x3 <= 311',
        '19*x1 + 2*x2 <= 174'
    ]
}
```

To solve this problem using Gurobi, we'll write the following Python code:
```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="rubber_bands")
x2 = m.addVar(vtype=GRB.INTEGER, name="monochrome_printers")
x3 = m.addVar(vtype=GRB.INTEGER, name="cans_of_coffee")

# Define the objective function
m.setObjective(5*x1 + 7*x2 + 4*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(19*x1 + 9*x3 >= 67, "usefulness_rubber_coffee")
m.addConstr(2*x2 + 9*x3 >= 105, "usefulness_printers_coffee")
m.addConstr(19*x1 + 2*x2 >= 61, "usefulness_rubber_printers")
m.addConstr(19*x1 + 2*x2 + 9*x3 >= 67, "total_usefulness_min")
m.addConstr(-8*x1 + 5*x3 >= 0, "rubber_coffee_constraint")
m.addConstr(-x2 + 8*x3 >= 0, "printers_coffee_constraint")
m.addConstr(19*x1 + 9*x3 <= 311, "usefulness_rubber_coffee_max")
m.addConstr(19*x1 + 2*x2 <= 174, "usefulness_rubber_printers_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Rubber bands: {x1.x}")
    print(f"Monochrome printers: {x2.x}")
    print(f"Cans of coffee: {x3.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```