## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ represents the number of printed art produced
- $x_2$ represents the number of paintings produced

The objective function is to maximize profit, which is $5x_1 + 8x_2$.

The constraints based on the given resources are:
- Printing time: $10x_1 + 0x_2 \leq 150$
- Framing time: $5x_1 + 15x_2 \leq 400$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'printed art'), ('x2', 'paintings')],
    'objective_function': '5*x1 + 8*x2',
    'constraints': [
        '10*x1 <= 150',
        '5*x1 + 15*x2 <= 400',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, name="printed_art")
    x2 = model.addVar(lb=0, name="paintings")

    # Objective function: maximize 5*x1 + 8*x2
    model.setObjective(5*x1 + 8*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(10*x1 <= 150, name="printing_time")
    model.addConstr(5*x1 + 15*x2 <= 400, name="framing_time")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of printed art: {x1.varValue}")
        print(f"Number of paintings: {x2.varValue}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```