## Step 1: Define the symbolic representation of the problem

Let's denote the number of mini elephants as $x_1$ and the number of mini lions as $x_2$. The objective is to maximize profit, where the profit per mini elephant is $50 and per mini lion is $45. The constraints are that each mini elephant requires 10 units of clay and each mini lion requires 8 units of clay, with a total of 300 units of clay available. Additionally, the artist can make at most 33 animals total.

## Step 2: Formulate the objective function and constraints

The objective function to maximize is $50x_1 + 45x_2$. The constraints are:
1. $10x_1 + 8x_2 \leq 300$ (clay constraint)
2. $x_1 + x_2 \leq 33$ (total animals constraint)
3. $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints, as the number of animals cannot be negative)

## 3: Symbolic representation in the required format

```json
{
'sym_variables': [('x1', 'mini elephants'), ('x2', 'mini lions')],
'objective_function': '50*x1 + 45*x2',
'constraints': [
    '10*x1 + 8*x2 <= 300',
    'x1 + x2 <= 33',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 4: Convert the problem into Gurobi code

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='mini_elephants', lb=0, vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='mini_lions', lb=0, vtype=gurobi.GRB.INTEGER)

    # Set the objective function
    model.setObjective(50 * x1 + 45 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(10 * x1 + 8 * x2 <= 300, name='clay_constraint')
    model.addConstr(x1 + x2 <= 33, name='total_animals_constraint')

    # Optimize the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: mini elephants = {x1.varValue}, mini lions = {x2.varValue}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```