## Step 1: Define the symbolic representation of the problem
Let's denote:
- $x_1$ as the number of units of cherry tomatoes,
- $x_2$ as the number of units of cocktail tomatoes.

The objective is to minimize the cost, which is $3x_1 + 4x_2$.

## Step 2: Translate the constraints into symbolic notation
The constraints based on the requirements are:
- Sugar: $5x_1 + 2.5x_2 \geq 350$,
- Acid: $1.5x_1 + 3x_2 \geq 250$,
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$.

## 3: Write down the symbolic representation of the optimization problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'units of cherry tomatoes'), ('x2', 'units of cocktail tomatoes')],
'objective_function': '3*x1 + 4*x2',
'constraints': [
    '5*x1 + 2.5*x2 >= 350',
    '1.5*x1 + 3*x2 >= 250',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="cherry_tomatoes", lb=0)
    x2 = model.addVar(name="cocktail_tomatoes", lb=0)

    # Define the objective function
    model.setObjective(3*x1 + 4*x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(5*x1 + 2.5*x2 >= 350, name="sugar_constraint")
    model.addConstr(1.5*x1 + 3*x2 >= 250, name="acid_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal cost: {model.objVal}")
        print(f"Units of cherry tomatoes: {x1.x}")
        print(f"Units of cocktail tomatoes: {x2.x}")
    else:
        print("The model is infeasible")

solve_tomato_paste_problem()
```