## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of containers of Regular Synthetic oil.
- $x_2$ represents the number of containers of Premium Synthetic oil.

## Step 2: Formulate the objective function
The profit per container of Regular Synthetic is $17.4 and the profit per container of Premium Synthetic is $11.1. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 17.4x_1 + 11.1x_2 \]

## 3: Define the constraints
The factory has limited amounts of substances A, B, and C:
- Substance A: $25x_1 + 10x_2 \leq 1000$
- Substance B: $40x_1 + 25x_2 \leq 500$
- Substance C: $36x_1 + 40x_2 \leq 900$
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of containers cannot be negative.

## 4: Symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'containers of Regular Synthetic'), ('x2', 'containers of Premium Synthetic')],
'objective_function': '17.4*x1 + 11.1*x2',
'constraints': [
    '25*x1 + 10*x2 <= 1000',
    '40*x1 + 25*x2 <= 500',
    '36*x1 + 40*x2 <= 900',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Convert the problem into Gurobi code
Now, let's convert this into Gurobi code in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="Regular_Synthetic", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="Premium_Synthetic", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(17.4 * x1 + 11.1 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(25 * x1 + 10 * x2 <= 1000, name="Substance_A")
    model.addConstr(40 * x1 + 25 * x2 <= 500, name="Substance_B")
    model.addConstr(36 * x1 + 40 * x2 <= 900, name="Substance_C")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Containers of Regular Synthetic: {x1.varValue}")
        print(f"Containers of Premium Synthetic: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```