To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. 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, which can be represented by the objective function $50x_1 + 45x_2$. 

The constraints are:
- Each mini elephant requires 10 units of clay, and each mini lion requires 8 units of clay. The artist has available 300 units of clay, so we have $10x_1 + 8x_2 \leq 300$.
- Due to time constraints, the artist can make at most 33 animals total, which gives us $x_1 + x_2 \leq 33$.
- Since the number of mini elephants and lions cannot be negative, we also have $x_1 \geq 0$ and $x_2 \geq 0$.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'number of mini elephants'), ('x2', 'number of mini lions')],
    'objective_function': '50*x1 + 45*x2',
    'constraints': ['10*x1 + 8*x2 <= 300', 'x1 + x2 <= 33', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this optimization problem using Gurobi in Python, we will use the following code:
```python
from gurobipy import *

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

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

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

# Add constraints
m.addConstr(10*x1 + 8*x2 <= 300, "clay_constraint")
m.addConstr(x1 + x2 <= 33, "time_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of mini elephants: {x1.x}")
    print(f"Number of mini lions: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found.")
```