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

Let's define:
- $y_1$ as the number of hybrid models made per day,
- $y_2$ as the number of electric models made per day.

The objective is to maximize profit, given by $4500y_1 + 5500y_2$, subject to the following constraints:
1. Daily demand for hybrid models: $y_1 \leq 9$
2. Daily demand for electric models: $y_2 \leq 5$
3. Total production capacity: $y_1 + y_2 \leq 10$
4. Non-negativity constraints: $y_1 \geq 0$, $y_2 \geq 0$

Thus, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('y1', 'number of hybrid models made per day'), ('y2', 'number of electric models made per day')],
  'objective_function': '4500*y1 + 5500*y2',
  'constraints': ['y1 <= 9', 'y2 <= 5', 'y1 + y2 <= 10', 'y1 >= 0', 'y2 >= 0']
}
```

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

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

# Define variables
y1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="hybrid_models")
y2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="electric_models")

# Set the objective function
m.setObjective(4500*y1 + 5500*y2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(y1 <= 9, "hybrid_demand")
m.addConstr(y2 <= 5, "electric_demand")
m.addConstr(y1 + y2 <= 10, "total_production")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Hybrid models: {y1.x}")
    print(f"Electric models: {y2.x}")
    print(f"Maximum profit: ${4500*y1.x + 5500*y2.x:.2f}")
else:
    print("No optimal solution found")
```