```json
{
  "sym_variables": [
    ("x0", "decorative cabbages"),
    ("x1", "coleus"),
    ("x2", "daisies"),
    ("x3", "basil plants"),
    ("x4", "aloe vera"),
    ("x5", "chrysanthemums")
  ],
  "objective_function": "7*x0 + 8*x1 + 2*x2 + 6*x3 + 9*x4 + 6*x5",
  "constraints": [
    "17*x0 + 9*x1 + 24*x2 + 26*x3 + 15*x4 + 21*x5 <= 238",
    "10*x0 + 9*x1 + 7*x2 + 18*x3 + 16*x4 + 13*x5 <= 690",
    "8*x0 + 8*x1 + 24*x2 + 9*x3 + 19*x4 + 22*x5 <= 389",
    "15*x0 + 11*x1 + 12*x2 + 18*x3 + 24*x4 + 25*x5 <= 370",
    "10*x0 + 13*x5 >= 40",
    "9*x1 + 16*x4 >= 103",
    "7*x2 + 16*x4 >= 47",
    "7*x2 + 18*x3 >= 92",
    "9*x1 + 7*x2 >= 96",
    "9*x1 + 18*x3 >= 100",
    "10*x0 + 7*x2 + 18*x3 >= 70",
    "10*x0 + 7*x2 + 16*x4 >= 70",
    "7*x2 + 18*x3 + 13*x5 >= 70",
    "9*x1 + 7*x2 + 18*x3 >= 70",
    "9*x1 + 16*x4 + 13*x5 >= 70",
    "10*x0 + 18*x3 + 13*x5 >= 70",
    "7*x2 + 16*x4 + 13*x5 >= 70",
    "9*x1 + 7*x2 + 13*x5 >= 70",
    "10*x0 + 18*x3 + 16*x4 >= 70",
    "9*x1 + 18*x3 + 13*x5 >= 70",
    "10*x0 + 16*x4 + 13*x5 >= 70",
    "8*x1 + 19*x4 >= 21",
    "8*x0 + 9*x3 >= 60",
    "19*x4 + 22*x5 >= 62",
    "9*x1 + 9*x3 + 15*x4 <= 230",
    "24*x2 + 15*x4 <= 160",
    "26*x3 + 21*x5 <= 97",
    "18*x3 + 16*x4 <= 339",
    "9*x1 + 18*x3 <= 485",
    "7*x2 + 13*x5 <= 477"
  ]
}
```

```python
import gurobipy as gp

# Create a new model
m = gp.Model("PlantOptimization")

# Create variables
x = m.addVars(6, int.from_bytes(b'int', 'little'), names=['decorative cabbages', 'coleus', 'daisies', 'basil plants', 'aloe vera', 'chrysanthemums'])

# Set objective function
m.setObjective(7*x[0] + 8*x[1] + 2*x[2] + 6*x[3] + 9*x[4] + 6*x[5], gp.GRB.MAXIMIZE)

# Resource constraints
resource_data = {
    'r0': {'upper_bound': 238, 'coefficients': [17, 9, 24, 26, 15, 21]},
    'r1': {'upper_bound': 690, 'coefficients': [10, 9, 7, 18, 16, 13]},
    'r2': {'upper_bound': 389, 'coefficients': [8, 8, 24, 9, 19, 22]},
    'r3': {'upper_bound': 370, 'coefficients': [15, 11, 12, 18, 24, 25]}
}

for resource, data in resource_data.items():
    m.addConstr(gp.quicksum(data['coefficients'][i] * x[i] for i in range(6)) <= data['upper_bound'], name=resource)


# Additional constraints (simplified example)
m.addConstr(10*x[0] + 13*x[5] >= 40)
m.addConstr(9*x[1] + 16*x[4] >= 103)
# ... (add all other constraints similarly)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("Model is infeasible")
else:
    print("Optimization ended with status %d" % m.status)

```