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

Let's denote:
- $x_1$ as the number of cans from Omega
- $x_2$ as the number of cans from Omini

The objective is to minimize the cost: $30x_1 + 40x_2$

The constraints are:
- $3x_1 + 5x_2 \geq 30$ (at least 30 units of water)
- $5x_1 + 6x_2 \geq 35$ (at least 35 units of detergent)
- $6x_1 + 5x_2 \geq 40$ (at least 40 units of bleach)
- $x_1, x_2 \geq 0$ (non-negativity constraints, as the number of cans cannot be negative)

## Step 2: Convert the problem into a Gurobi code

We will use the Gurobi library in Python to solve this linear programming problem.

## Step 3: Write down the symbolic representation in the required format

```json
{
    'sym_variables': [('x1', 'cans from Omega'), ('x2', 'cans from Omini')],
    'objective_function': '30*x1 + 40*x2',
    'constraints': [
        '3*x1 + 5*x2 >= 30',
        '5*x1 + 6*x2 >= 35',
        '6*x1 + 5*x2 >= 40',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 4: Implement the problem in Gurobi Python code

```python
import gurobi as gp

# Create a new model
m = gp.Model("Laundromat_Mixture")

# Define the variables
x1 = m.addVar(name="Omega_cans", lb=0, vtype=gp.GRB.INTEGER)  # cans from Omega
x2 = m.addVar(name="Omini_cans", lb=0, vtype=gp.GRB.INTEGER)  # cans from Omini

# Define the objective function
m.setObjective(30*x1 + 40*x2, gp.GRB.MINIMIZE)

# Define the constraints
m.addConstr(3*x1 + 5*x2 >= 30, name="water_constraint")
m.addConstr(5*x1 + 6*x2 >= 35, name="detergent_constraint")
m.addConstr(6*x1 + 5*x2 >= 40, name="bleach_constraint")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Minimum cost: ${30*x1.x + 40*x2.x:.2f}")
else:
    print("No optimal solution found")
```