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 using these variables.

Let's define:
- \(x_1\) as the number of cans from Omega.
- \(x_2\) as the number of cans from Omini.

The objective is to minimize the total cost, which can be represented by the objective function: 
\[30x_1 + 40x_2\]

The constraints based on the content requirements are:
1. Water: \(3x_1 + 5x_2 \geq 30\)
2. Detergent: \(5x_1 + 6x_2 \geq 35\)
3. Bleach: \(6x_1 + 5x_2 \geq 40\)

Additionally, since we cannot have a negative number of cans, we also have:
- \(x_1 \geq 0\)
- \(x_2 \geq 0\)

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'Number of cans from Omega'), ('x2', 'Number of 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']
}
```

To solve this problem using Gurobi in Python, we'll write the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="Omega_Cans")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="Omini_Cans")

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

# Add constraints
m.addConstr(3*x1 + 5*x2 >= 30, "Water_Requirement")
m.addConstr(5*x1 + 6*x2 >= 35, "Detergent_Requirement")
m.addConstr(6*x1 + 5*x2 >= 40, "Bleach_Requirement")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of cans from Omega: {x1.x}")
    print(f"Number of cans from Omini: {x2.x}")
    print(f"Total cost: {m.objVal}")
else:
    print("No optimal solution found.")

```