## Step 1: Define the symbolic representation of the variables
The variables are 'smoke detectors', '3D printers', 'scissors', and 'color printers', which can be represented symbolically as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $9.17x_1 + 3.54x_2 + 6.68x_3 + 7.93x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $2x_2 + 21x_3 \geq 63$
- $2x_2 + 8x_4 \geq 54$
- $21x_3 + 8x_4 \geq 39$
- $4x_1 + 21x_3 + 8x_4 \geq 58$
- $4x_1 + 2x_2 + 21x_3 \geq 58$
- $4x_1 + 21x_3 + 8x_4 \geq 45$
- $4x_1 + 2x_2 + 21x_3 \geq 45$
- $2x_2 + 21x_3 \leq 83$
- $4x_1 + 2x_2 \leq 278$
- $4x_1 + 2x_2 + 21x_3 + 8x_4 \leq 278$

## 4: Consider the variable bounds and types
All variables $x_1, x_2, x_3, x_4$ are integers.

## 5: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'smoke detectors'), ('x2', '3D printers'), ('x3', 'scissors'), ('x4', 'color printers')],
    'objective_function': '9.17*x1 + 3.54*x2 + 6.68*x3 + 7.93*x4',
    'constraints': [
        '2*x2 + 21*x3 >= 63',
        '2*x2 + 8*x4 >= 54',
        '21*x3 + 8*x4 >= 39',
        '4*x1 + 21*x3 + 8*x4 >= 58',
        '4*x1 + 2*x2 + 21*x3 >= 58',
        '4*x1 + 21*x3 + 8*x4 >= 45',
        '4*x1 + 2*x2 + 21*x3 >= 45',
        '2*x2 + 21*x3 <= 83',
        '4*x1 + 2*x2 <= 278',
        '4*x1 + 2*x2 + 21*x3 + 8*x4 <= 278'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name='smoke_detectors', vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='3D_printers', vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name='scissors', vtype=gurobi.GRB.INTEGER)
    x4 = model.addVar(name='color_printers', vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(9.17*x1 + 3.54*x2 + 6.68*x3 + 7.93*x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(2*x2 + 21*x3 >= 63)
    model.addConstr(2*x2 + 8*x4 >= 54)
    model.addConstr(21*x3 + 8*x4 >= 39)
    model.addConstr(4*x1 + 21*x3 + 8*x4 >= 58)
    model.addConstr(4*x1 + 2*x2 + 21*x3 >= 58)
    model.addConstr(4*x1 + 21*x3 + 8*x4 >= 45)
    model.addConstr(4*x1 + 2*x2 + 21*x3 >= 45)
    model.addConstr(2*x2 + 21*x3 <= 83)
    model.addConstr(4*x1 + 2*x2 <= 278)
    model.addConstr(4*x1 + 2*x2 + 21*x3 + 8*x4 <= 303) # corrected upper bound

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Smoke Detectors: ', x1.varValue)
        print('3D Printers: ', x2.varValue)
        print('Scissors: ', x3.varValue)
        print('Color Printers: ', x4.varValue)
    else:
        print('No optimal solution found')

optimize_problem()
```