Here's the Gurobi code to solve the optimization problem:

```python
from gurobipy import Model, GRB

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

# Create variables
chrysanthemums = m.addVar(vtype=GRB.INTEGER, name="chrysanthemums")
daisies = m.addVar(vtype=GRB.INTEGER, name="daisies")
aloe_vera = m.addVar(vtype=GRB.INTEGER, name="aloe_vera")
hydrangeas = m.addVar(vtype=GRB.INTEGER, name="hydrangeas")
cherry_trees = m.addVar(vtype=GRB.INTEGER, name="cherry_trees")

# Set objective function
m.setObjective(
    1 * chrysanthemums**2
    + 6 * chrysanthemums * daisies
    + 5 * chrysanthemums * aloe_vera
    + 1 * chrysanthemums * hydrangeas
    + 9 * chrysanthemums * cherry_trees
    + 7 * daisies**2
    + 4 * daisies * aloe_vera
    + 6 * daisies * hydrangeas
    + 1 * daisies * cherry_trees
    + 6 * aloe_vera**2
    + 6 * aloe_vera * hydrangeas
    + 3 * aloe_vera * cherry_trees
    + 7 * hydrangeas**2
    + 3 * hydrangeas * cherry_trees
    + 1 * cherry_trees**2
    + 6 * chrysanthemums
    + 1 * daisies
    + 8 * aloe_vera
    + 9 * hydrangeas
    + 9 * cherry_trees,
    GRB.MAXIMIZE,
)

# Add constraints
m.addConstr(9 * chrysanthemums + 7 * daisies + 7 * aloe_vera + 22 * hydrangeas + 3 * cherry_trees <= 182, "beauty_rating")
m.addConstr(22 * chrysanthemums + 17 * daisies + 18 * aloe_vera + 7 * hydrangeas + 18 * cherry_trees <= 385, "dollar_cost")
m.addConstr(chrysanthemums**2 + daisies**2 >= 35, "c1")
m.addConstr(chrysanthemums + cherry_trees >= 32, "c2")
m.addConstr(daisies + cherry_trees >= 12, "c3")
m.addConstr(aloe_vera + hydrangeas >= 21, "c4")
m.addConstr(chrysanthemums + daisies + hydrangeas >= 30, "c5")
m.addConstr(22 * chrysanthemums**2 + 7 * hydrangeas**2 >= 33, "c6")
m.addConstr(18 * aloe_vera**2 + 7 * hydrangeas**2 >= 25, "c7")
m.addConstr(22 * chrysanthemums + 18 * cherry_trees >= 46, "c8")
m.addConstr(7 * hydrangeas + 18 * cherry_trees >= 34, "c9")
m.addConstr(22 * chrysanthemums**2 + 17 * daisies**2 >= 48, "c10")
m.addConstr(18 * aloe_vera**2 + 18 * cherry_trees**2 >= 32, "c11")
m.addConstr(7 * chrysanthemums - 5 * cherry_trees >= 0, "c12")
m.addConstr(9 * chrysanthemums + 7 * daisies <= 129, "c13")
m.addConstr(7 * daisies**2 + 22 * hydrangeas**2 <= 128, "c14")
m.addConstr(9 * chrysanthemums + 22 * hydrangeas <= 105, "c15")
m.addConstr(22 * hydrangeas + 3 * cherry_trees <= 138, "c16")
m.addConstr(9 * chrysanthemums + 7 * aloe_vera <= 179, "c17")
m.addConstr(9 * chrysanthemums + 3 * cherry_trees <= 104, "c18")
m.addConstr(7 * aloe_vera**2 + 3 * cherry_trees**2 <= 145, "c19")
m.addConstr(7 * daisies**2 + 22 * hydrangeas**2 + 3 * cherry_trees**2 <= 147, "c20")
m.addConstr(9 * chrysanthemums**2 + 7 * aloe_vera**2 + 3 * cherry_trees**2 <= 84, "c21")
m.addConstr(9 * chrysanthemums + 7 * daisies + 7 * aloe_vera + 22 * hydrangeas + 3 * cherry_trees <= 84, "c22")
m.addConstr(22 * chrysanthemums + 7 * hydrangeas <= 257, "c23")
m.addConstr(17 * daisies + 18 * aloe_vera <= 246, "c24")
m.addConstr(22 * chrysanthemums + 18 * cherry_trees <= 259, "c25")
m.addConstr(22 * chrysanthemums + 17 * daisies + 18 * aloe_vera + 7 * hydrangeas + 18 * cherry_trees <= 259, "c26")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    for v in m.getVars():
        print(f"{v.varName}: {v.x}")
    print(f"Objective value: {m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("Model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}.")

```
