## Step 1: Define the symbolic representation of the variables
The variables are 'cafeteria chairs', 'black pens', and 'usb flash drives'. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : 'cafeteria chairs'
- $x_1$ : 'black pens'
- $x_2$ : 'usb flash drives'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $7x_0 + 8x_1 + 4x_2$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $9x_0 + 12x_1 \leq 51$
- $9x_0 + 12x_1 + 8x_2 \leq 51$
- $x_0 + x_2 \leq 35$
- $x_1 + x_2 \leq 67$
- $x_0 + x_1 + x_2 \leq 67$
- $x_0 \geq 0$ and is an integer
- $x_1 \geq 0$ and is an integer
- $x_2 \geq 0$ and is an integer

## 5: Correct and simplify constraints based on the problem description
Upon reviewing, notice that the problem description already implies non-negativity and integrality for all variables, so we focus on the given constraints:
- The total combined sustainability score from cafeteria chairs plus black pens has to be 51 at maximum: $9x_0 + 12x_1 \leq 51$
- The total combined sustainability score from cafeteria chairs, black pens and usb flash drives has to be no more than 51: $9x_0 + 12x_1 + 8x_2 \leq 51$
- You have to spend no more than 35 dollars on cafeteria chairs plus usb flash drives: $x_0 + x_2 \leq 35$
- black pens and usb flash drives must cost a total of at most 67 dollars: $x_1 + x_2 \leq 67$
- You have to spend no greater than $67 on cafeteria chairs plus black pens plus usb flash drives: $x_0 + x_1 + x_2 \leq 67$

## 6: Create the Gurobi model and variables
We will use Gurobi to solve this optimization problem.

## 7: Implement the Gurobi code
```python
import gurobi as gp

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

# Define the variables
x0 = m.addVar(name="cafeteria_chairs", vtype=gp.GRB.INTEGER)
x1 = m.addVar(name="black_pens", vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="usb_flash_drives", vtype=gp.GRB.INTEGER)

# Define the objective function
m.setObjective(7*x0 + 8*x1 + 4*x2, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(9*x0 + 12*x1 <= 51, name="sustainability_score_chairs_pens")
m.addConstr(9*x0 + 12*x1 + 8*x2 <= 51, name="sustainability_score_all")
m.addConstr(x0 + x2 <= 35, name="cost_chairs_usb")
m.addConstr(x1 + x2 <= 67, name="cost_pens_usb")
m.addConstr(x0 + x1 + x2 <= 67, name="total_cost")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Cafeteria Chairs: ", x0.varValue)
    print("Black Pens: ", x1.varValue)
    print("USB Flash Drives: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 8: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'cafeteria chairs'), ('x1', 'black pens'), ('x2', 'usb flash drives')],
    'objective_function': '7*x0 + 8*x1 + 4*x2',
    'constraints': [
        '9*x0 + 12*x1 <= 51',
        '9*x0 + 12*x1 + 8*x2 <= 51',
        'x0 + x2 <= 35',
        'x1 + x2 <= 67',
        'x0 + x1 + x2 <= 67'
    ]
}
```