## Step 1: Define the symbolic representation of the variables
The variables are 'scissors' and 'color printers', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to minimize is $9.7x_1 + 3.74x_2$.

## Step 3: List the constraints in symbolic notation
The constraints are:
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity, but we also have integer and general constraints)
- $1x_1 + 19x_2 \geq 45$ (spend at least $45)
- $1x_1 + 19x_2 \leq 116$ (spend no more than $116)
- $30x_1 + 7x_2 \geq 119$ (usefulness rating at least 119)
- $30x_1 + 7x_2 \leq 319$ (usefulness rating at most 319)
- $21x_1 + 23x_2 \geq 36$ (sustainability score at least 36)
- $21x_1 + 23x_2 \leq 117$ (sustainability score at most 117, but also has an upper bound of 83 specifically)
- $21x_1 + 23x_2 \leq 83$ (sustainability score upper bound of 83)
- $-7x_1 + 7x_2 \geq 0$ (relationship between scissors and color printers)
- $x_1$ is an integer (integer constraint for scissors)
- $x_2$ is an integer (integer constraint for color printers, but the problem states nonfractional which implies integer)

## 4: Correct and refine the constraints based on the problem description
- The problem mentions $x_1$ (scissors) and $x_2$ (color printers) must be integers and nonfractional respectively, which implies both should be integers.
- The constraint for spending is $1x_1 + 19x_2 \geq 45$ and $1x_1 + 19x_2 \leq 116$.
- Usefulness rating: $30x_1 + 7x_2 \geq 119$ and $30x_1 + 7x_2 \leq 319$.
- Sustainability score: $21x_1 + 23x_2 \geq 36$ and $21x_1 + 23x_2 \leq 83$.

## 5: Formulate the symbolic representation
```json
{
    'sym_variables': [('x1', 'scissors'), ('x2', 'color printers')],
    'objective_function': '9.7*x1 + 3.74*x2',
    'constraints': [
        '1*x1 + 19*x2 >= 45',
        '1*x1 + 19*x2 <= 116',
        '30*x1 + 7*x2 >= 119',
        '30*x1 + 7*x2 <= 319',
        '21*x1 + 23*x2 >= 36',
        '21*x1 + 23*x2 <= 83',
        '-7*x1 + 7*x2 >= 0',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 6: Write the Gurobi code
```python
import gurobi

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

    # Define variables
    scissors = model.addVar(name="scissors", vtype=gurobi.GRB.INTEGER)
    color_printers = model.addVar(name="color_printers", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(9.7 * scissors + 3.74 * color_printers, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(scissors + 19 * color_printers >= 45)
    model.addConstr(scissors + 19 * color_printers <= 116)
    model.addConstr(30 * scissors + 7 * color_printers >= 119)
    model.addConstr(30 * scissors + 7 * color_printers <= 319)
    model.addConstr(21 * scissors + 23 * color_printers >= 36)
    model.addConstr(21 * scissors + 23 * color_printers <= 83)
    model.addConstr(-7 * scissors + 7 * color_printers >= 0)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Scissors: {scissors.varValue}")
        print(f"Color Printers: {color_printers.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```