## Step 1: Define the symbolic representation of the variables
The variables are 'verbenas' and 'tulips', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is $9 \times \text{verbenas} + 8 \times \text{tulips}$, which in symbolic notation is $9x_1 + 8x_2$.

## 3: Convert the constraints into symbolic notation
The constraints are:
1. $10 \times \text{verbenas} + 19 \times \text{tulips} \geq 30 \times 144$ (since 30 sq. ft = 30 * 144 sq. in, but we need to consider the actual constraint which is at least 30 sq. ft or 4320 sq. in for verbenas and tulips together, not individually, my mistake, we use 30*144),
2. $7 \times \text{verbenas} - 4 \times \text{tulips} \geq 0$,
3. $10 \times \text{verbenas} + 19 \times \text{tulips} \leq 82 \times 144$ (since 82 ft^2 = 82 * 144 in^2),
4. $\text{verbenas}, \text{tulips}$ are integers and non-negative.

Correcting and properly accounting for units and given constraints:
- The planting space constraint for verbenas and tulips together is $10x_1 + 19x_2 \geq 4320$ (30 sq ft * 144 sq in/sq ft),
- The constraint that they occupy at least 30 sq ft is inherently satisfied by $10x_1 + 19x_2 \geq 4320$,
- The upper bound is $10x_1 + 19x_2 \leq 82 \times 144$,
- $7x_1 - 4x_2 \geq 0$.

## 4: Define the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'verbenas'), ('x2', 'tulips')],
'objective_function': '9*x1 + 8*x2',
'constraints': [
    '10*x1 + 19*x2 >= 4320',
    '7*x1 - 4*x2 >= 0',
    '10*x1 + 19*x2 <= 11808',
    'x1 >= 0', 'x2 >= 0', 'x1 % 1 == 0', 'x2 % 1 == 0'
]
}
```

## 5: Implement the problem in Gurobi
```python
import gurobi

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

    # Define variables
    verbenas = model.addVar(name="verbenas", vtype=gurobi.GRB.INTEGER)
    tulips = model.addVar(name="tulips", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(9 * verbenas + 8 * tulips, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(10 * verbenas + 19 * tulips >= 4320, name="space_requirement")
    model.addConstr(7 * verbenas - 4 * tulips >= 0, name="verbenas_vs_tulips")
    model.addConstr(10 * verbenas + 19 * tulips <= 11808, name="total_space")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Verbenas: {verbenas.varValue}")
        print(f"Tulips: {tulips.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```