To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints in terms of these variables.

Let's denote:
- $x_0$ as the number of lanyards,
- $x_1$ as the number of color printers.

The objective function is to minimize $3x_0 + 3x_1$.

The constraints are:
1. Usefulness rating constraint: $4x_0 + 23x_1 \geq 61$
2. Storage space constraint (minimum): $23x_0 + 9x_1 \geq 48$
3. Constraint on lanyards and color printers: $6x_0 - 6x_1 \geq 0$
4. Maximum usefulness rating: $4x_0 + 23x_1 \leq 79$
5. Maximum storage space constraint: $23x_0 + 9x_1 \leq 81$
6. Non-negativity and integer constraints: $x_0, x_1 \geq 0$ and both are integers.

Symbolic representation:
```json
{
    'sym_variables': [('x0', 'lanyards'), ('x1', 'color printers')],
    'objective_function': '3*x0 + 3*x1',
    'constraints': [
        '4*x0 + 23*x1 >= 61',
        '23*x0 + 9*x1 >= 48',
        '6*x0 - 6*x1 >= 0',
        '4*x0 + 23*x1 <= 79',
        '23*x0 + 9*x1 <= 81'
    ]
}
```

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Add variables
x0 = m.addVar(vtype=GRB.INTEGER, name="lanyards")
x1 = m.addVar(vtype=GRB.INTEGER, name="color_printers")

# Set the objective function
m.setObjective(3*x0 + 3*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x0 + 23*x1 >= 61, "usefulness_rating")
m.addConstr(23*x0 + 9*x1 >= 48, "min_storage_space")
m.addConstr(6*x0 - 6*x1 >= 0, "lanyards_color_printers_ratio")
m.addConstr(4*x0 + 23*x1 <= 79, "max_usefulness_rating")
m.addConstr(23*x0 + 9*x1 <= 81, "max_storage_space")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Lanyards: {x0.x}")
    print(f"Color Printers: {x1.x}")
else:
    print("No optimal solution found.")
```