## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to various constraints. The variables are:
- geraniums: $x_1$
- bean stalks: $x_2$
- chili plants: $x_3$
- vincas: $x_4$
- hydrangeas: $x_5$
- boxwoods: $x_6$
- basil plants: $x_7$

## Step 2: Formulate the objective function
The objective function to maximize is:
$5.11x_1x_2 + 5.56x_1x_4 + 7.89x_1x_7 + 1.39x_2x_3 + 5.31x_2x_7 + 2.76x_4x_5 + 8.55x_5^2 + 2.85x_6^2 + 3.25x_7^2 + 6.16x_3 + 3.57x_6$

## Step 3: List the constraints
Constraints include:
- $17x_1 + 18x_2 + 15x_3 + 9x_4 + 18x_5 + 15x_6 + 12x_7 \leq 249$ (beauty rating)
- $9x_1 + 17x_2 + 14x_3 + 5x_4 + 15x_5 + 20x_6 + 9x_7 \leq 361$ (yield)
- $8x_1 + 14x_2 + 11x_3 + 4x_4 + 12x_5 + 7x_6 + 9x_7 \leq 304$ (dollar cost)
- $18x_5 + 12x_7 \geq 26$ (beauty rating constraint)
- $17x_1^2 + 18x_5^2 \geq 17$ (beauty rating constraint)
- ... (many more constraints)

## 4: Convert to Gurobi code
Given the complexity and the number of constraints, directly writing out all constraints and the objective function in Gurobi Python interface is necessary.

```python
import gurobi as gp

# Define variables
m = gp.Model()
x1 = m.addVar(name="geraniums", vtype=gp.GRB.INTEGER)
x2 = m.addVar(name="bean_stalks", vtype=gp.GRB.INTEGER)
x3 = m.addVar(name="chili_plants", vtype=gp.GRB.INTEGER)
x4 = m.addVar(name="vincas", vtype=gp.GRB.INTEGER)
x5 = m.addVar(name="hydrangeas", vtype=gp.GRB.INTEGER)
x6 = m.addVar(name="boxwoods", vtype=gp.GRB.INTEGER)
x7 = m.addVar(name="basil_plants", vtype=gp.GRB.INTEGER)

# Objective function
m.setObjective(5.11*x1*x2 + 5.56*x1*x4 + 7.89*x1*x7 + 1.39*x2*x3 + 5.31*x2*x7 + 
               2.76*x4*x5 + 8.55*x5**2 + 2.85*x6**2 + 3.25*x7**2 + 6.16*x3 + 3.57*x6, 
               gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(17*x1 + 18*x2 + 15*x3 + 9*x4 + 18*x5 + 15*x6 + 12*x7 <= 249)
m.addConstr(9*x1 + 17*x2 + 14*x3 + 5*x4 + 15*x5 + 20*x6 + 9*x7 <= 361)
m.addConstr(8*x1 + 14*x2 + 11*x3 + 4*x4 + 12*x5 + 7*x6 + 9*x7 <= 304)
m.addConstr(18*x5 + 12*x7 >= 26)
m.addConstr(17*x1**2 + 18*x5**2 >= 17)

# Add many more constraints here...

# Optimize
m.optimize()

# Print solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Geraniums: ", x1.varValue)
    print("Bean stalks: ", x2.varValue)
    print("Chili plants: ", x3.varValue)
    print("Vincas: ", x4.varValue)
    print("Hydrangeas: ", x5.varValue)
    print("Boxwoods: ", x6.varValue)
    print("Basil plants: ", x7.varValue)
else:
    print("No solution found")
```