## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'lanyards' and 'color printers', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $3x_1 + 3x_2$. The constraints are:
- $4x_1 + 23x_2 \geq 61$
- $23x_1 + 9x_2 \geq 48$
- $6x_1 - 6x_2 \geq 0$
- $4x_1 + 23x_2 \leq 79$
- $23x_1 + 9x_2 \leq 81$
- $x_1 \leq 124 / 4 = 31$ (implicit upper bound from $r0$)
- $x_2 \leq 109 / 9 = 12.111...$ (implicit upper bound from $r1$, but we consider integer constraints)
- $x_1$ is an integer
- $x_2$ is an integer

## 2: Convert the problem into a symbolic representation
The symbolic variables are:
- $x_1$ for 'lanyards'
- $x_2$ for 'color printers'

The objective function is: $3x_1 + 3x_2$

The constraints are:
- $4x_1 + 23x_2 \geq 61$
- $23x_1 + 9x_2 \geq 48$
- $6x_1 - 6x_2 \geq 0$
- $4x_1 + 23x_2 \leq 79$
- $23x_1 + 9x_2 \leq 81$
- $x_1 \geq 0$ and $x_2 \geq 0$ (implicit non-negativity)

## 3: Express the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'lanyards'), ('x2', 'color printers')],
    'objective_function': '3*x1 + 3*x2',
    'constraints': [
        '4*x1 + 23*x2 >= 61',
        '23*x1 + 9*x2 >= 48',
        '6*x1 - 6*x2 >= 0',
        '4*x1 + 23*x2 <= 79',
        '23*x1 + 9*x2 <= 81',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Implement the problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="lanyards", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="color_printers", vtype=gurobi.GRB.INTEGER)

    # Objective function: minimize 3*lanyards + 3*color_printers
    model.setObjective(3*x1 + 3*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4*x1 + 23*x2 >= 61)
    model.addConstr(23*x1 + 9*x2 >= 48)
    model.addConstr(6*x1 - 6*x2 >= 0)
    model.addConstr(4*x1 + 23*x2 <= 79)
    model.addConstr(23*x1 + 9*x2 <= 81)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Lanyards: {x1.varValue}")
        print(f"Color Printers: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```