To solve this optimization problem, we first need to identify and translate all given information into a symbolic representation. This includes defining variables, an objective function, and constraints.

Let's denote:
- $x_1$ as the number of zucchini vines,
- $x_2$ as the number of pansies.

The objective function is to maximize: $9.72x_1 + 5.74x_2$

Given constraints are:
1. Each zucchini vine requires 7 sq. in of planting space, and each pansy also requires 7 sq. in.
2. The beauty rating of a zucchini vine is 6, and for a pansy, it's 1.
3. Total planting space must be at least 25 sq. ft (which equals 3600 sq. in) but no more than 40 sq. ft (4800 sq. in).
4. The total combined beauty rating must be at least 20 but no more than 45.
5. $-7x_1 + 7x_2 \geq 0$

Symbolic representation of the problem:
```json
{
    'sym_variables': [('x1', 'zucchini vines'), ('x2', 'pansies')],
    'objective_function': '9.72*x1 + 5.74*x2',
    'constraints': [
        '7*x1 + 7*x2 >= 3600',  # Minimum total planting space in sq. in
        '7*x1 + 7*x2 <= 4800',  # Maximum total planting space in sq. in
        '6*x1 + x2 >= 20',      # Minimum combined beauty rating
        '6*x1 + x2 <= 45',      # Maximum combined beauty rating
        '-7*x1 + 7*x2 >= 0'     # Additional constraint
    ]
}
```

Now, let's write the Gurobi code for this problem. Note that we are dealing with integer variables since both zucchini vines and pansies must be in whole numbers.

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.INTEGER, name="zucchini_vines")
x2 = m.addVar(vtype=GRB.INTEGER, name="pansies")

# Set the objective function
m.setObjective(9.72*x1 + 5.74*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*x1 + 7*x2 >= 3600, "min_total_space")
m.addConstr(7*x1 + 7*x2 <= 4800, "max_total_space")
m.addConstr(6*x1 + x2 >= 20, "min_beauty_rating")
m.addConstr(6*x1 + x2 <= 45, "max_beauty_rating")
m.addConstr(-7*x1 + 7*x2 >= 0, "additional_constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Zucchini vines: {x1.x}")
    print(f"Pansies: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```