## Step 1: Define the symbolic representation of the problem

Let's denote the number of spoons, forks, and knives as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Translate the natural language description into a symbolic representation

The objective is to maximize revenue, which can be represented as $2x_1 + 3x_2 + 4x_3$.

## Step 3: Define the constraints based on the available resources

The company has limited steel and rubber. The constraints can be represented as:
- $1x_1 + 1.5x_2 + 2x_3 \leq 400$ (steel constraint)
- $2x_1 + 1.5x_2 + 1x_3 \leq 500$ (rubber constraint)
Also, $x_1 \geq 0$, $x_2 \geq 0$, and $x_3 \geq 0$ since the number of items cannot be negative.

## 4: Write down the symbolic representation in the required format

```json
{
    'sym_variables': [('x1', 'spoons'), ('x2', 'forks'), ('x3', 'knives')], 
    'objective_function': '2*x1 + 3*x2 + 4*x3', 
    'constraints': [
        '1*x1 + 1.5*x2 + 2*x3 <= 400', 
        '2*x1 + 1.5*x2 + 1*x3 <= 500', 
        'x1 >= 0', 
        'x2 >= 0', 
        'x3 >= 0'
    ]
}
```

## 5: Convert the symbolic representation into Gurobi code

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="spoons", lb=0, ub=float('inf'), obj=2)
    x2 = model.addVar(name="forks", lb=0, ub=float('inf'), obj=3)
    x3 = model.addVar(name="knives", lb=0, ub=float('inf'), obj=4)

    # Add constraints
    model.addConstr(x1 + 1.5 * x2 + 2 * x3 <= 400, name="steel_constraint")
    model.addConstr(2 * x1 + 1.5 * x2 + x3 <= 500, name="rubber_constraint")

    # Set the objective function
    model.setObjective(x1.obj * x1.varName + x2.obj * x2.varName + x3.obj * x3.varName, gurobi.GRB.MAXIMIZE)

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Spoons: {x1.x}")
        print(f"Forks: {x2.x}")
        print(f"Knives: {x3.x}")
        print(f"Max Revenue: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```